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Buildings 2019, 9(7), 166;

An Overview of Climate Change and Building Energy: Performance, Responses and Uncertainties
Department of Civil, Architectural and Environmental Engineering, Drexel University, 3141 Chestnut Street, Curtis 251, Philadelphia, PA 19104, USA
Author to whom correspondence should be addressed.
Received: 13 June 2019 / Accepted: 4 July 2019 / Published: 8 July 2019


It is becoming increasingly crucial to develop methods and strategies to assess building performance under the changing climate and to yield a more sustainable and resilient design. However, the outputs of climate models have a coarse spatial and temporal resolution and cannot be used directly in building energy simulation tools. This paper reviews methods to develop fine spatial and temporal weather files that incorporate climate emissions scenarios by means of downscaling. An overview of the climate change impact on building energy performance is given, and potential adaptation and mitigation factors in response to the changing climate in the building sector are presented. Also, methods to reflect, propagate, and partition main sources of uncertainties in both weather files and buildings are summarized, and a sample approach to propagate the uncertainties is demonstrated.
sustainable & resilient; weather files; responses; uncertainties

1. Introduction

Buildings, through their lifecycle, play an important role in energy consumption and Greenhouse Gas (GHG) emissions [1] both directly (i.e., through its operation [2]) and indirectly (i.e., through the production of building materials [3], construction processes [4,5], transportation [6], rehabilitation, and demolition [7]). While these impacts underscore the importance of applying a dynamic approach [8,9,10] in life cycle assessments, the primary share of both energy consumption and environmental impacts takes place during a building’s operation [2,3,4,5,6,11,12]. The operation phase of a building can be 50–100 years or more, and the performance of a building during this time is not static. For example, the thermal conductivity of construction [13] and insulation materials [14] are directly influenced by outdoor weather conditions, which are not only sensitive to short term changes (i.e., seasonal variability) but also long-term climate dynamics. The focus of this paper is climate change impacts on building operation and performance, which are assessed using building simulation tools. Building simulation tools solve dynamic equations with respect to building thermodynamics and physics and are widely used to assess energy performance. The use of dynamic simulation tools becomes even more complicated with the changing climate, and given the many sources of uncertainties (from both the building and climate), strategies to evaluate their performance under future scenarios are necessary. Yet climate models lack the temporal resolution to be utilized by simulation tools, which require weather files in an hourly format. In this paper, we review methods to generate future hourly weather files from climate change emission scenarios for building simulations. We also highlight key findings in the literature on future building performance as a result of climate change. First, a summary of climate models and methods to generate fine spatial and temporal resolution weather files is presented. Next, predicted impacts on building energy performance using a combination of downscaled weather files and building simulation tools are reviewed. We find there is a gap in the scholarship addressing the combined effect of design condition and weather files on future building performance. Finally, potential adaptation and mitigation factors in response to the changing climate are reviewed as well as methods to reflect, propagate and partition main sources of uncertainties stemming from both buildings and weather files. We close by offering a sample probabilistic approach for propagating uncertainties in building energy performance under future climate conditions.

2. Climate Projections

Increased GHG emissions produced by human activities interact with the climate’s balance [15] and has changed climate trends. While the changes may vary by decade, surface temperatures have increased since 1880 (Figure 1).
Figure 1 shows the annual surface temperature anomaly, which is the difference between the long-term temperature and the observed temperature and shows an approximately 1 °C increase since 1880 globally. The increasing trend in temperatures is attributed to increased GHG emissions primarily due to the burning of fossil fuels. An approximately 100ppm increase in CO2 concentration has been measured globally since 1978 [17]. The Intergovernmental Panel on Climate Change (IPCC) developed climate emissions scenarios to enhance our understanding of how the future might look [18]. The IPCC is an intergovernmental body that reviews technical and scientific reports around the world related to climate change. The United Nations Environmental Programme and the World Meteorological Organization (WMO) established the IPCC [19]. The first IPCC scenario was developed in 1990, followed by the Third Assessment Report (TAR), the Fourth Assessment Report (AR4) and the most recent Fifth Assessment Report (AR5), where four different scenarios, called the Representative Concentration Pathways (RCPs), were introduced.
The first step for climate prediction is defining future scenarios. The emissions scenarios are incorporated into General Circulation Models (GCM) and Regional Circulation Models (RCM), which are models used to understand climate behavior and to forecast probable changes [20]. GCMs/RCMs simulate the changes in the climate over time and illustrate how climate components (surface, atmospheric, and oceanic) interact with each other in order to develop an understanding of their variability. Figure 2 shows the trend of different scenarios from the TAR, projected to 2100. They are called projections because future GHG emissions are unknown. The projections present a snapshot of the possibilities that may occur in the future based on current emissions status and assumptions about socioeconomic factors such as population, economic, and technological developments [21].
In Figure 2 the most extreme scenario (A2) projects an approximate 3.5 °C increase, a 2.5 °C for the A1B scenario and the most conservative scenario (B1) shows a 1.5 °C increase in global temperature by the 21st century. Based on the emissions scenarios in Figure 2, it is apparent that the differences between most emission scenarios are not expected to be significant prior to 2040. This is mainly due to the inertia of the climate and the ~100-year lifespan of CO2 emissions [19]. The North American Regional Climate Change Assessment Program [23] and the IPCC data distribution center [24] provide outputs from different climate models for different locations and time scales using various emission scenarios. However, these data are at best on a 3-hourly temporal resolution (see Appendix A) and have a coarse spatial resolution, obviating their direct application in building simulations. To obtain a fine spatial and temporal resolution, the use of downscaling techniques is required. Downscaling can be done dynamically (using RCMs) or statistically (stochastic methods or morphing). Moazami et al. (2019) provides a detailed description of advantages and disadvantages of dynamic and statistical downscaling methods [25]. The next section reviews different methods used to generate the necessary weather information for building performance analysis using statistical downscaling techniques. Common adaptation and mitigation strategies in response to building vulnerabilities associated with climate change risks are then reviewed. Risk in this context refers to the probability of occurrence of extreme temperatures and vulnerability as the probability of the building sector being exposed to the risk [26].

3. Weather Files

Buildings are a significant contributor to global warming, and their performance is highly vulnerable to climate variations. Therefore, it is essential to anticipate their performance under the changing climate [27]. A majority of the building simulation tools require weather files as input data [28]. Typical Year (TY) weather files which represent past weather observations are commonly used to avoid using multiple single year weather data [29]. There are two main ways to produce a TY. One is by using a whole year or 12 consecutive months as a representative of the historical observations, and the other is the selection of months by different criteria in which would combine to a whole year [20]. A brief summary on the most commonly used TY files is presented below.

3.1. Current TY Files

Depending on the amount of historical data available, location and the purpose of the data, different methods of typical weather data creation exist (Danish, Festa & Ratto, Finkelstein & Schafer, etc.). Some of the most common types of TYs are Typical Meteorological Year (TMY), Test Reference Year (TRY), Example Weather Year (EWY), Weather Year for Energy Calculations (WYEC), and Design Summer Year (DSY). The TYs differ in their method for selecting representative months that are concatenated to produce a typical year. These sets of data are useful for designing buildings and for comparing their performance in different locations or against different types of buildings.
The TMY file contains diurnal and seasonal variations that reflect the climate for a location and is mainly used in North America [28]. The first set of TMY files was created using data (1952–1975) from 248 locations in 1978 using National Climate Data Center (NCDC) and uses a Finkelstein & Schafer (FS) approach, which is an empirical selection of the typical months out of several years of historical observation. TMY2 data were an updated version of the primary TMY using data from 1961 to 1990 and were presented in 1994 [30]. The most current data sets are TMY3 which uses the input data from 1976–2005, 1961–1990, and 1991–2005 of the National Solar Radiation Data Base, which contains the data for 1020 locations [31]. The TMY3 files contain 68 variables such as global horizontal radiation, direct normal radiation, total sky cover, dry-bulb temperature, relative humidity/dew-point temperature, and wind speed/direction [28].
The European Commission of Energy Efficiency and Renewables provides TMY files worldwide using an updated database from 2005–2015 [32] with a data source obtained from the Satellite Application Facility on Climate Monitoring and European Centre for Medium-Range Weather Forecasts. The TRY and DSY were presented by the Charted Institution of Building Services Engineers in collaboration with Exeter University which are available for 14 locations around the UK. The process of choosing a TRY is similar to the TMY but with different weighting factors (i.e., the importance of each weather parameter). The DSY uses a simpler method. Instead of using average months, the DSY uses a single continual year with the third hottest summer. The main issue of this method is that it does not consider wind speed or solar radiation. However, there have been many efforts to improve these types of files such as the development of the Probabilistic of Design Summer Year (PDSY), the Summer Reference Year (SRY), the Extreme Meteorological Year (XMY), Untypical Meteorological Year (UMY), and Hot Summer Year (HSY). The details of each are presented by [20]. The WYEC selection of months is based on temperature, using the closest value to the long-term mean temperatures. In this method, wind speed is not considered. For free running buildings, this would not offer a suitable method for weather data selection [33].
The use of any of these methods is limited to the amount of data available and the location of weather stations. Sometimes data extracted are from weather stations that are far from major cities that cannot be an exact representative of the local area and where factors such as urban heat island can present discrepancies [34]. Alternative measures to overcome the lack of data exist, such as extracting data from other weather stations closest to the area under examination, using regression methods to estimate data from the years that are not available; using cloud cover data (for the case of solar radiation), and using data from satellites [35]. A summary table of selected research on TY files is presented in Table 1.

3.2. Future Weather Files

The American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) states that typical weather data should not be used to assess building performance in the future [44]. Generating future typical weather data for building design tools is mostly done by different synthetic weather generators or by statistical techniques such as the morphing procedure [29]. Due to the limitations of historical weather data, synthetic weather generators are more prevalent. Synthetic weather data resembles historical weather behavior and gives insight about cases where data are not significant or are not available; they can simulate meteorological variables for different time periods. The WMO suggests an averaging period of 30 years for defining the baseline climate, which can be challenging if not enough historical weather data is available. Weather generators can be used to produce enough data to assess the probability of occurrence of each variable in the future [20] (e.g., WGEN, CLIGEN, ClimGen, CRU-WG, Met&Roll, WeaGETS, and the KnnCAD). Building energy simulator tools commonly used in the U.S. require weather files in the format of TMY and EnergyPlus Weather (EPW) files. The EPW file is used by the energy simulation modeling software developed by the Department of Energy (DOE). They are a type of weather file that compile with the TMY file dataset. There exists a rich body of research on the creation of future weather data used in building performance analyses and weather generators capable of providing such spatial and temporal requirement which take into account emissions scenarios [45]. A summary of selected research on future weather files for building analyses is presented in Table 2.
In addition to Table 2, several weather generators exist that produce future hourly weather data, such as the Urban Weather Gen (UWG) [64], the Advanced WEather GENerator (AWE-GEN) [65,66], the Climate Change World Weather Generator (CCWorldWeatherGen) [49,67], Meteonorm, and WeatherShift. The CCWorldWeatherGen, developed with Excel, uses the morphing procedure to downscale the outputs of a GCM. Belcher et al. (2005) developed a method called “morphing” to create future hourly weather data to be used in buildings thermal simulations [63]. The morphing technique uses shifting (same variance, different average) and stretching (same average, different variance) on the monthly averages of the weather variables. For instance, for dry bulb temperature, a combination of both is applied: both its mean (a shift) and diurnal changes (a stretch) are transformed. Jentsch et al. (2008) used this method to develop an accessible tool called the CCWorldWeatherGen [67]. The CCWorldWeatherGen uses the Hadley Center Coupled Model Version 3 of the Atmospheric-Ocean GCMs datasets and transforms present EPW or TMY files into future EPW or TMY files. It generates future weather files that capture average weather conditions of climate scenarios while preserving realistic weather sequences for any location around the world. Typical weather data for three future time slices are produced (2020, 2050, and 2080) and for the TAR A2 emission scenario. The CCWorldWeatherGen tool is based on the work of the Sustainable Energy Research Group. The Meteonorm software is a stochastic weather generator that uses an average of all available climate models in the IPCC AR4. It contains a comprehensive climate database which are derived from the Global Energy Balance Archive and WMO and NCDC [54], and can generate typical weather files for two periods of 1981–1990 and 1991–2010 worldwide. In addition, it uses three emission scenarios (A1B, B1, and A2) from the IPCC AR4 and produces outputs in various formats (e.g., TMY and EPW) for nine future time slices (2020-2100). The WeatherShift tool uses a probabilistic approach to create future hourly weather data using two emission scenarios (RCP4.5 and RCP8.5) from the IPCC AR5 for three future time periods (2035, 2065, and 2090). The AWE-GEN was created by the University of Michigan in 2007 and can produce hourly weather data from meteorological stations in the US [65]. It can generate hourly data for ten locations in the US and one location in Italy. The AWE-GEN weather generator is MATLAB-based and can reproduce low and high frequency characteristics of meteorological parameters. Data produced by this weather generator, however, are in raw format that need to be converted to either TMY or EPW files for building simulations. The UWG is also a MATLAB-based simulator that estimates hourly urban air temperature and humidity using weather data from local weather stations. It also captures urban heat island effects. Scholars have used these methods to assess the building performance under future climate conditions. Creating, modifying, and evaluating future weather data for the use of the built environment requires careful attention. There are ongoing studies to eliminate the uncertainties generated from future weather variabilities. In addition to the TY files, most building simulation tools require the use of design condition file, also known as Design Day (DD) files to size building equipment and evaluate their effectiveness under the changing climate. A brief explanation of the DD files is presented as follows.

3.3. Design Day

A design day describes a period with maximum conditions for which the Heating Ventilation and Air Conditioning (HVAC) system was designed to function (operational peak conditions) to maintain indoor conditions. DD files contain a statistical description of monthly and annual weather and are based on different percentiles for warm (0.4%, 1%, and 2%) and cold (99.6% and 99%) seasons [68]. The percentiles for cold seasons define outdoor conditions for a parameter and location which stays above the 99.6% and 99%, and for a warm season, defines outdoor conditions that stays above 0.4%, 1%, and 2% for all hours of the year. The information provided in a design day file consists of required weather parameters for sizing HVAC equipment for heating and cooling. For cooling, the DD files contain the 0.4%, 1%, and 2% dry bulb and its mean coincident wet bulb (MCWB) used to size chillers and air conditioners. The 0.4%, 1%, and 2% wet bulb and its mean coincident dry bulb and wet bulb are used for designing cooling towers and other evaporative coolers. These are especially useful for humidity control applications, such as desiccant cooling and dehumidification, cooling-based dehumidification, fresh-air ventilation systems, and enthalpy systems. For heating applications, the data contains the 99.6% and 99% dry bulb (DB) which is used to size heating equipment. The 99.6% and 99% dew point and its mean coincident dry bulb is used to size equipment for humidification. In addition, the mean coincident wind speed and prevailing coincident wind direction of the 0.4% DB which is used for estimating peak loads due to infiltration. The current design day files available for the United States are updated to 2013 and very few descriptions regarding the use of future scenarios for design day files are available. Without DD files to drive equipment modernization in the future, an increase of heating consumption projections in addition to excessive cooling requirements [69] is anticipated.

4. Building Performance

In urban areas, increased population growth and energy demand in buildings have resulted in greater energy consumption thereby driving the global share (75%) of GHG emissions [15]. Inefficient building construction and equipment and increased GHG emissions are impacted by climate change. Climate change presents not only changes in weather conditions but also imposes uncertainties on building performance. A building’s operation is subject to several driving forces such as climate, site and location, geometry, façade and fenestration, architectural design, internal loads, ventilation systems, heating and cooling equipment, and control units. In this section, climate is considered to be the primary driving force and whole building energy performance under the changing climate is reviewed. The significance of other factors (e.g. façade and fenestration improvements, internal loads, etc.) are considered as a response to the primary driving force (the changing climate). During its 50–100 years lifespan, a building may change its operational requirements [70]. Therefore, it is necessary to develop analytical methods to model building performance under future scenarios in response to climate change.

4.1. Energy Assessment

Sustainable building design has the potential to reduce GHG emissions more aggressively compared to other sectors [71]. The driving forces behind sustainable buildings are cost, energy efficiency, and thermal comfort, which must be satisfied in the present as well as under future conditions [72]. Common ways to approach this challenge are the use of Artificial Intelligence (AI), the degree-day method and the use of building energy simulation tools. AI-based approaches such as neural networks [73], fuzzy logic methods [74], and genetic algorithms [75], are used to forecast, model, and control [76] building consumption [77], along with evaluating conservation measures [78] and thermal comfort [79]. The degree-day method is a common way to measure seasonal severity and its impact on building energy consumption [80]. The degree day measures the number of days that the mean of the highest and lowest temperature of the day is higher (for cooling degree days) or lower (for heating degree days) compared to the base temperature. The base temperature is not the set-point temperature of the indoor environment, but the outdoor temperature at which the heat loss from the building equals the heat gain. It may vary depending on the environment, building type, purpose, and thermo-physical properties of the building [80]. The degree day method is also used by the DOE to categorize climate zones across the U.S. in addition to temperature and humidity.
In 2017, approximately 40% of the total energy consumption in the U.S. came from residential and commercial sectors [81]. Results from US Energy Information Administration’s 2012 Commercial Buildings Energy Consumption Survey show that between 1979 and 2012, the number of commercial buildings in the U.S. has increased from 3.8 million to 5.6 million. Assessment of energy performance of commercial and residential buildings under future climate scenarios has gained a lot of attention in recent years. Some commonly used building energy simulation tools that have been used to assess future building performance are EnergyPlus, BSim, DOE-2.1E, Tas, TRACE, HAP, Helios, Integrated Environment Solution (IES) Virtual Environment, TRNSYS, eQUEST, and ESP-r [82]. The energy simulation results of buildings modeled under future climate conditions, which stem from climate models for different emissions scenarios, using future weather files generated by means of downscaling techniques, are presented. Table 3 is a summary of the literature which use future typical weather files developed mainly by methods presented in Section 3.2 (morphing or stochastic generators) to assess building energy performance using building simulation tools for different regions. The location column in Table 3 presents the context for the case studies, which were either a continent (e.g., Europe), country, or a city of interest.
From Table 3, it is apparent that the performance of commercial and residential buildings under future climate has driven most of the research over the last decade. Depending on the location and case under study, the results vary in scale. Some studies revealed the impact of the changing climate to be insignificant (e.g., Germany residential [86] and Shanghai offices [110]). The results of buildings with specific building features also varies, for example NZEBs were found to be effective and less sensitive to the climate conditions in US residential building stock [93] but inefficient in the case of Montreal [84]. However, the majority of the studies showed an increase in total energy consumption: Australia (offices [83] and residential [55]), US (commercial and residential) [91], Burkina Faso (public buildings) [96], Hong Kong (offices and residential) [47], China (commercial) [51], Argentina (mid-income housing) [107], and Europe (offices) [108]. In addition, because climate models predict a warming climate on average, many studies revealed a shift towards increased cooling requirements: Hong Kong (offices and residential) [85], China (offices) [90], Portugal (commercial and residential) [94], Adelaide (residential) [111], UK (residential) [102], and US (offices and residential) [105]. The shift towards cooling evades natural ventilation strategies in some regions: US commercial and residential [92] and UK offices [95]. In addition, an increase in peak energy demand is expected. This poses an important risk to future electricity supply [105,106].
In more recent years, the impact of applying strategies to reduce consumption and increase the sustainability of the buildings is gaining attention. The effect of modernization and consideration of design days, however, has received less attention. Design day files have a direct impact on the building equipment sizing. When assessing buildings under future climate conditions, it is crucial to develop updated design day files that are associated to the weather files and evaluate the impacts considering modernization. The use of both typical weather files and DD files to assess building energy performance is necessary.
Figure 3 shows the relation between TMY files and DD files in assessing building energy consumption and demand for current and future conditions. For instance, in order to assess building equipment deficiencies of an existing building, the use of a current DD file and future DD file is necessary. Conversely, to assess existing building performance under future climate, a future TMY file along with current DD file is required. There is a gap in the literature addressing the combined effects of DD and weather files on future building performance. The next section reviews different conservation measures taken in the literature as a response to climate change.

4.2. Responses

Responses to climate change that are built on practices to reduce GHG emissions are described as mitigation strategies. Responses to climate change impacts are described as adaptation strategies. In either case, responses are either passive or active [112]. Passive responses may encompass bioclimatic concepts such as orientation, insulation, natural ventilation (etc.), used to reduce the thermal exchange between the interior and exterior providing comfort with limited need of heating/cooling and ventilation applications. Active responses are generally comprised of efficient use of HVAC technologies. Nevertheless, there is no single solution that accurately predicts building energy performance under future climate conditions, and the effect of adaptation and mitigations strategies may vary from case to case [113]. Instead, a set of adaptation and mitigation strategies are required for resilience planning in the built environment [97].
Programs such as Leadership in Energy and Environmental Design by the U.S. Green Building Council have addressed mitigation requirements for buildings [114]. However, it is necessary that adaptation and mitigation strategies towards climate change be introduced to current standard codes and policies for building designers. Standards need to be developed to enhance adaptive capacity, to suggest the range of acceptable changes, and to optimize occupant satisfaction [115]. For the latter, thermal comfort can vary with air temperature, humidity, wind velocity, and clothing, and may differ from person to person depending on their metabolic rate. The environmental variables can be controlled by designers and are standardized for all occupants. However, the metabolic rate of the human body changes from person to person. In addition, the ability of a person to tolerate different indoor conditions varies. This requires deeper scrutiny and presents high uncertainty in developing adaptive responses towards climate change. Strategies in the literature to reduce building vulnerability and improve thermal comfort are summarized in Table 4. The impacts of the responses vary across geographical location and case under study which reflects the complexity of identifying optimal measures. The uncertainty of the response factors themselves also impacts the outcomes. For example, Wang et al. (2012) showed the annual site energy of office buildings in the US varied with a range of −11.3% to 6.8% from plug load improvements, −5.3% to 8.4% from lighting control measures and a −5.7% to 9.5% for VAV damper minimum setting control [116]. Coley & Kershaw (2012) reflect input variations by presenting different percentiles of outcomes. They showed for a school in UK by 2050 the 50th percentile of their results yielded a −1.25 °C, −1.23 °C, −0.45 °C, −4.8 °C and −0.03 °C reduction in internal temperatures when implementing shading, solar control glass, earlier day schedules, night ventilation and window opening respectively [98]. Many of the factors presented in Table 4 are viable response strategies. For example, Frank (2005) showed for office and residential buildings in Zurich, improvement in thermal insulation reduces heating demand by 81% [87]; Holmes & Hacker (2007) showed that night cooling can reduce hours above 28°C by 40% [89]; Wan et al. (2012) demonstrated that reducing Window to Wall Ratio (WWR) for a case in office buildings in China resulted in up to 5% reduction in annual average building energy use [90]; Chow & Levermore (2010) suggested using Phase Change Materials (PCM) as a response to the changing climate [95]; and Wan et al. (2011) concluded that a COP of 5.5 would be required for the case of office building in Hong Kong to alleviate the climate change impacts [99]. The effect of a combination of measures is also assessed by scholars [89,90,91,95]. For example, Loveland and Brown (1996) showed for the case of Seattle a 43% reduction in cooling loads from a combination of LD, insulation and shading measures, which is higher than the cumulative results of each individual response [91].
While a survey of building industry representatives highlighted the importance of adaptation strategies in buildings [127], most of the research conducted in response strategies consist of climate mitigation rather than adaptation. A balance between adaptation and mitigation is needed. However, when applying adaptation and mitigation it is necessary to understand incremental [128], transformational [129], synergies, trade-offs, and conflicts of the response measures [112]. For instance, the impact of applying mitigation measures may result in a higher reduction in energy consumption all together (a synergy), or an increase in energy consumption compared to the impact of each measure individually (a conflict), or a competing result necessitating re-evaluation of priorities (a trade-off). In many cases, it is difficult to draw a line to distinguish between adaptation and mitigation strategies. For instance, some adaptation measures may have a mitigating outcome and present co-benefits. Also, adaptation strategies should not be confused with coping strategies, which are short term responses that individuals take temporarily [130] (i.e., clothing, schedules).
The literature shows useful information in terms of case studies (sectors under study) and the effectiveness (i.e., reduction in consumption) of different response strategies. These, however, could benefit from an explicit functional unit (i.e., tons of CO2) to contextualize the overall impact of the response. Most studies lack the spatial and temporal effect of the responses and measures to monitor the impacts. For instance, it is unclear if the adaptation and mitigation strategies can be aggregated for regional or global benefits. It is also less well known whether the strategies can be realized over a long period of time [131].
Overall, the impact of global warming on buildings may vary for different regions. For instance, subtropical regions may face overheating in summer and result in increased energy consumption and thermal discomfort. Even in cold climate regions, the impact of increased overheating in summer outweighs the impact of moderate winters in terms of energy consumption. These impacts complicate the uncertainties of assessing building performance and may require a case by case study to enhance our understanding of the impacts. The next section reviews existing uncertainties and describes strategies to address them.

5. Uncertainties

Most buildings were constructed before adaptation or mitigation standards or regulations were implemented, and therefore, strategies to improve their energy performance over time and in response to a changing climate are required [132]. This means new methods are required to provide information to designers to make decisions that guide design and performance. However, what decisions will yield optimal results? There is a lack of information or a lack of knowledge about the consequences of design decisions for buildings that will operate 50 to 100 years in the future [132]. Building simulation tools might predict future building conditions, providing a general understanding of the energy and thermal performance of the structure. However, what are the most effective strategies to ensure energy conservation or thermal comfort? The decision making process is not trivial when changes in the frequency, extent, timing, and rapidity of hazards (i.e., extreme temperature) or the amount of uncertainty in the output are very high [132].
Uncertainty analysis in this context reflects how the uncertainties in the input parameter is propagated in the output. It can provide reliability to the design parameters for the overall design. Uncertainties can be due to the building simulation technique, the building’s physical characteristics [119,123,133], user behavior [123], the method of TY generation, data source, or availability of historical data, the emissions scenarios, climate models, initial conditions, natural climate variability, or downscaling techniques [21,46,116,133,134]. A summary of potential uncertainties in conducting building performance assessment by means of downscaled weather data and building simulation tools is presented in Figure 4.
Efforts to partition the total uncertainty of the output of the climate models to their sources are ongoing (e.g., using the Analysis of Variance (ANOVA) model). Decomposing uncertainties of different climate parameters such as global mean air surface temperatures [135,136,137] and regional precipitation levels [138] is a way of reflecting the total uncertainty into its sources. In the near future, uncertainties due to the output of climate models are dominant, while in the end-century, scenario uncertainties become dominant [135,136,138,139]. This is mainly due to socioeconomic assumptions used to develop emissions scenarios. Different scenarios can be used to describe a range likelihood of occurrences [140]. For future climate projections and uncertainties due to emissions scenarios, the probability functions from all available climate scenarios can be considered [141]. Initial conditions are often set as preindustrial conditions, which represents a period before changes in the climate were not attributable to human activity. However, there is little information from that period about specific climate variables, and simulations are used to generate hypothetical initial conditions and referred to as control runs [21]. Uncertainties that are due to the response of different climate models can be treated by using output results from different GCMs/RCMs [141]. The uncertainty of climate variability (i.e., in mean and standard deviation) can be described by probabilistic approaches [141]. This uncertainty increases when applying weather files for regional scales and hourly time periods to be used in building simulations [142]. The use of statistical techniques in addition to the dynamic approaches of assessing building performance can provide a range of possible outcomes that could take into account uncertainties in this part. This would require methods to narrow down the uncertainty in order to make a critical decision.
One way to address this would be to propagate the effect of the input uncertainty by creating a set of probability distributions. Hayes (2011) [140] presents five ways that a probabilistic approach can be implemented: expert opinion, standard parametric results, bootstrapping, maximum likelihood, and Bayesian approach. Bootstrapping is a method that can be used when the population is unknown and only sample data are available. The method would resample the data to characterize its variability and produce a population distribution of the sample given. In other words, it propagates uncertainty in the inputs through the model to characterize uncertainty in the output. However, this method requires the existence of two important elements: first, the input parameters and second, a dynamic/statistical model to be used to propagate the results. When there is doubt about the appropriate distribution (and input parameters) to be used, then different distributions should be assumed and their effect should be analyzed. To test whether a distribution suits the data, a comparison can be made to choose the best fit [143]. Or this can be done by determining the statistical parameter with distribution fitting techniques. For instance, one method is to minimize the difference between the empirical Cumulative Distribution Function (CDF) and the CDF of the fitted distribution. However, probabilities are not always easy to assess. This adds to the uncertainty of the approach and requires deep understanding of the variables under study to assess specific probabilities. One is to assume each variable has an equal chance of occurrence. Another way is to set up a survey among scientists to assess their prediction in the range of the possibilities [132]. Each step on the propagation method adds to the total uncertainties.
The statistical model for buildings can be developed by producing enough energy outputs from different climate inputs by using dynamic simulation tools. Then the model must be regressed between weather variables and the energy outcomes to associate the input variables to the outputs of the building simulation tool. The regression model can be used as the second element of the uncertainty propagation. Figure 5 shows a sample approach to propagate the uncertainties for building performance under future climate conditions. The arrows reflect a step that is part of the uncertainty analysis but also adds to the uncertainties.
In Figure 5, the first step is to generate enough future TMY and DD files to capture the future climate conditions based on different scenarios for different time periods (i.e., 2020, 2050, and 2080) using either a morphing technique or weather generators. Using statistical parameter fitting techniques, a distribution can be fitted to the weather variables (i.e., the distribution of dry bulb temperature). The generate weather files can then be used as input file to a sample building using a building simulation tool (e.g., EnergyPlus), and large number of outputs (e.g., electricity consumption) can be generated. The association between the output (energy) and input (weather variables) can be determined using statistical regression models (e.g., linear or nonlinear). With the model and distribution determined, a random sampling technique (i.e., Monte Carlo [144]) can be used to propagate the input uncertainty to the output and draw a range of possible occurrences in the future. Probabilistic information within future projections reflect the likelihood of occurrence of future climate. This method can be done for any other variable depending on the sensitivity of that variable to building energy performance. In general, while imperfect, sampling-based methods are commonly used to propagate the uncertainties.

6. Conclusions and Discussion

Today’s buildings must be future-proofed against climate impacts. To do this, it is necessary to understand how buildings will perform in the future and to develop and refine climate resilience policies to mitigate climate risks and vulnerabilities. Conventional building energy assessment approaches do not consider the changing climate. It is necessary to develop new strategies and methods to advance resiliency of the building and the ongoing sustainability of the built environment. This article reviewed current approaches by scholars to understand how buildings will perform in the future. Methods to develop granular spatial and temporal weather files that can be incorporated in building simulation tools were presented and their impact on the building stock along with adapting and mitigating measures were reviewed. It was found that there is a gap in the literature on the effect of equipment modernization and the consideration of design days. Design day files have a direct impact on the building equipment sizing. When assessing buildings under future climate conditions, it is crucial to develop updated design day files and weather files, and to evaluate the impacts of modernizing HVAC equipment.
Uncertainties regarding the development of the typical year files, climate models, emissions scenarios, internal weather variability, downscaling techniques, building simulation, user behavior, and physical characteristics of buildings were presented (and summarized in Figure 4). Common methods to reflect, propagate, and partition uncertainties were described. A sample approach to uncertainty analysis using downscaled future typical weather files in building simulation tools was presented. In uncertainty analysis, a probabilistic approach that reflects all possible scenarios is necessary to obtain a comprehensive understanding of the current state and possible outcomes in the future. This provides decision makers and designers with knowledge and information to select a suitable fit for their design goals. Overall, given the level of uncertainty regarding future climate conditions, current approaches are not adequate. The building design and analysis research community must drive the effort to integrate climate models into meteorological weather data [145]. This is needed to increase awareness of how buildings will need to be designed and operated in the future. Moreover, a comprehensive assessment of building typologies under extreme conditions [25] to guide future building codes and standards [146] is necessary. These efforts must also include an understanding of adaptive capacity under extreme conditions [147], as well as economic feasibility [148,149] assessments of potential response factors. Advancing climate-ready decision making [150] will promote resilience processes and policies for buildings under climate change.

Author Contributions

Authors contribution are as follow: Conceptualization, H.Y. and S.H.; Investigation, H.Y.; Writing-Original Draft Preparation, H.Y. and S.H.; Writing-Review & Editing, H.Y. and S.H.; Supervision, S.H.


This research received no external funding.


The authors are thankful to Patrick Gurian for his technical guidance and support.

Conflicts of Interest

The authors declare no conflict of interest. This research received no significant funding which could have influenced the outcome.


GHGGreenhouse Gas
IPCCIntergovernmental Panel on Climate Change
UNEPUnited Nations Environmental Programme
WMOWorld Meteorological Organization
TARThird Assessment Report
AR4Fourth Assessment Report
AR5Fifth Assessment Report
GCMGeneral Circulation Models
RCMRegional Circulation Models
RCPRepresentative Concentration Pathway
NARCCAPNorth American Regional Climate Change Assessment Program
TYTypical Year
TMYTypical Meteorological Year
TRYTest Reference Year
EWYExample Weather Year
WYECWeather Year for Energy Calculations
DSYDesign Summer Year
NCDCNational Climate Data Center
FSFinkelstein & Schafer
NSRDBNational Solar Radiation Data Base
ECEEREuropean Commission of Energy Efficiency and Renewables
CM SAFSatellite Application Facility on Climate Monitoring
ECMWFEuropean Centre for Medium-Range Weather Forecasts
CIBSECharted Institution of Building Services Engineers
PDSYProbabilistic of Design Summer Year
SRYSummer Reference Year
XMYExtreme Meteorological Year
UMYUntypical Meteorological Year
HSYHot Summer Year
ASHRAEAmerican Society of Heating, Refrigerating and Air-Conditioning Engineers
DOEDepartment of Energy
EPWEnergyPlus Weather
CRMCloud Radiation Model
EFMYErsatz Future Metrological Year
UWGUrban Weather Gen
AWE-GENAdvanced WEather GENerator
CCWorldWeatherGenClimate Change World Weather Generator
HadCM3Hadley Center Coupled Model Version 3
SERGSustainable Energy Research Group
GEBAGlobal Energy Balance Archive
WMOWorld Meteorological Organization
DDDesign Day
HVACHeating Ventilation and AirConditioning
MCWBmean coincident wet bulb
DBDry Bulb
COP21Conference of the Parties
SDGsustainability development goals
NZEBNet Zero Energy Building
LLDlight load density
LCFLow Carbon Futures
LEEDLeadership in Energy and Environmental Design
USGBCU.S. Green Building Council
WWRwindow to wall ratio
SPTset point temperature
SHCGsolar heat gain coefficient
LDlighting density
EEequipment efficiency
ANOVAAnalysis of Variance
CBECSCommercial Buildings Energy Consumption Survey
EIAEnergy Information Administration
PCMPhase Change Material
CDFCumulative Distribution Function

Appendix A

Table A1. Spatial resolutions of the IPCC climate models.
Table A1. Spatial resolutions of the IPCC climate models.
Beijing Climate Center (China)BCC-CSM1.13 hr3 hr3 hr3 hr
Beijing Normal University (China)BNU-ESM3 hr3 hr-3 hr
Canadian Centre for Climate Modelling and Analysis (Canada)CanESM26 hr6 hr-6 hr
Centro Euro-Mediterraneo sui Cambiamenti Climatici (Italy)CMCC-CM3 hr--3 hr
Centre National de Recherches Météorologiques (France)CNRM-CM53 hr3 hr-3 hr
Commonwealth Scientific and Industrial Research Organization/Bureau of Meteorology (Australia) ACCESS1.03 hr--3 hr
Commonwealth Scientific and Industrial Research Organization/Queensland Climate Centre(Australia)CSIRO-Mk3.6.0Day6 hrDay6 hr
The First Institute of Oceanography, SOA (China)FIO-ESMM 1MMM
EC-EARTH consortium published at Irish Centre for High-End Computing (Netherlands/Ireland)EC-EARTH3 hr3 hr-3 hr
Russian Academy of Sciences, Institute of Numerical Mathematics (Russia)INMCM4.03 hr--3 hr
Institut Pierre Simon Laplace (France)IPSL-CM5A-LR3 hr3 hr3 hr3 hr
Institute of Atmospheric Physics, Chinese Academy of Sciences (China)FGOALS-g23 hr--3 hr
Atmosphere and Ocean Research Institute (Japan)MIROC53 hr3 hr3 hr3 hr
Met Office Hadley Centre (UK)HadGEM2-ES3 hr3 hr3 hr3 hr
Max Planck Institute for Meteorology (Germany)MPI-ESM-LRDay6 hr-6 hr
Meteorological Research Institute (Japan)MRI-CGCM33 hr3 hr3 hr3 hr
NASA/GISS (Goddard Institute for Space Studies) (USA)GISS-E2-RM3 hrMM
National Center for Atmospheric Research (USA)CCSM43 hr3 hr3 hr3 hr
Bjerknes Centre for Climate Research, Norwegian Meteorological Institute (Norway)NorESM1-M3 hr3 hr3 hr3 hr
National Institute of Meteorological Research (South Korea)HadGEM2-AOMMMM
Geophysical Fluid Dynamics Laboratory (USA)GFDL-ESM2M3 hr3 hr3 hr3 hr
1 Monthly.


  1. Berardi, U. A cross-country comparison of the building energy consumptions and their trends. Resour. Conserv. Recycl. 2017, 123, 230–241. [Google Scholar] [CrossRef]
  2. Sharma, A.; Saxena, A.; Sethi, M.; Shree, V. Life cycle assessment of buildings: A review. Renew. Sustain. Energy Rev. 2011, 15, 871–875. [Google Scholar] [CrossRef]
  3. Ramesh, T.; Prakash, R.; Shukla, K. Life cycle energy analysis of buildings: An overview. Energy Build. 2010, 42, 1592–1600. [Google Scholar] [CrossRef]
  4. Asdrubali, F.; Baldassarri, C.; Fthenakis, V. Life cycle analysis in the construction sector: Guiding the optimization of conventional Italian buildings. Energy Build. 2013, 64, 73–89. [Google Scholar] [CrossRef]
  5. Bilec, M.M.; Ries, R.J.; Matthews, H.S. Life-cycle assessment modeling of construction processes for buildings. J. Infrastruct. Syst. 2009, 16, 199–205. [Google Scholar] [CrossRef]
  6. Rossi, B.; Marique, A.-F.; Glaumann, M.; Reiter, S. Life-cycle assessment of residential buildings in three different European locations, basic tool. Build. Environ. 2012, 51, 395–401. [Google Scholar] [CrossRef]
  7. Blengini, G.A. Life cycle of buildings, demolition and recycling potential: A case study in Turin, Italy. Build. Environ. 2009, 44, 319–330. [Google Scholar] [CrossRef]
  8. Collinge, W.O.; Landis, A.E.; Jones, A.K.; Schaefer, L.A.; Bilec, M.M. Dynamic life cycle assessment: Framework and application to an institutional building. Int. J. Life Cycle Assess. 2013, 18, 538–552. [Google Scholar] [CrossRef]
  9. Collinge, W.O.; Landis, A.E.; Jones, A.K.; Schaefer, L.A.; Bilec, M.M. Productivity metrics in dynamic LCA for whole buildings: Using a post-occupancy evaluation of energy and indoor environmental quality tradeoffs. Build. Environ. 2014, 82, 339–348. [Google Scholar] [CrossRef]
  10. Collinge, W.O.; Rickenbacker, H.J.; Landis, A.E.; Thiel, C.L.; Bilec, M.M. Dynamic Life Cycle Assessments of a Conventional Green Building and a Net Zero Energy Building: Exploration of Static, Dynamic, Attributional, and Consequential Electricity Grid Models. Environ. Sci. Technol. 2018, 52, 11429–11438. [Google Scholar] [CrossRef] [PubMed]
  11. Cabeza, L.F.; Rincón, L.; Vilariño, V.; Pérez, G.; Castell, A. Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: A review. Renew. Sustain. Energy Rev. 2014, 29, 394–416. [Google Scholar] [CrossRef]
  12. Ochoa, L.; Hendrickson, C.; Matthews, H.S. Economic input-output life-cycle assessment of US residential buildings. J. Infrastruct. Syst. 2002, 8, 132–138. [Google Scholar] [CrossRef]
  13. Berardi, U.; Tronchin, L.; Manfren, M.; Nastasi, B. On the effects of variation of thermal conductivity in buildings in the Italian construction sector. Energies 2018, 11, 872. [Google Scholar] [CrossRef]
  14. Berardi, U.; Naldi, M. The impact of the temperature dependent thermal conductivity of insulating materials on the effective building envelope performance. Energy Build. 2017, 144, 262–275. [Google Scholar] [CrossRef]
  15. United Nations Human Settlements Programme. Cities and Climate Change: Global Report on Human Settlements 2011; Routledge: London, UK, 2011. [Google Scholar]
  16. National Oceanic and Atmospheric Administrative. National Centers for Environmental Information. Available online: (accessed on 20 May 2019).
  17. European Environmental Agency. Atmospheric Greenhouse Gas Concentrations. Available online: (accessed on 20 May 2019).
  18. Pachauri, R.K.; Allen, M.R.; Barros, V.R.; Broome, J.; Cramer, W.; Christ, R.; Church, J.A.; Clarke, L.; Dahe, Q.; Dasgupta, P.; et al. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: Geneva, Switzerland, 2014. [Google Scholar]
  19. Vong, N.K. Climate Change and Building Energy Use: Evaluating the Impact of Future Weather on Building Energy Performance in Tropical Regions; University of Hawai’i at Manoa: Ann Arbor, MI, USA, 2016; p. 192. [Google Scholar]
  20. Herrera, M.; Natarajan, S.; Coley, D.A.; Kershaw, T.; Ramallo-González, A.P.; Eames, M.; Fosas, D.; Wood, M. A review of current and future weather data for building simulation. Build. Serv. Eng. Res. Technol. 2017, 38, 602–627. [Google Scholar] [CrossRef]
  21. Nik, V.M.; Kalagasidis, A.S. Impact study of the climate change on the energy performance of the building stock in Stockholm considering four climate uncertainties. Build. Environ. 2013, 60, 291–304. [Google Scholar] [CrossRef]
  22. Nakicenovic, N.; Alcamo, J.; Grubler, A.; Riahi, K.; Roehrl, R.A.; Rogner, H.H.; Victor, N. Special Report on Emissions Scenarios (SRES), A Special Report of Working Group III of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2000. [Google Scholar]
  23. North American Regional Climate Change Assessment Program. Available online: (accessed on 10 April 2019).
  24. Intergovernmental Panel on Climate Change Data Distribution Center. Available online: (accessed on 10 April 2019).
  25. Moazami, A.; Nik, V.M.; Carlucci, S.; Geving, S. Impacts of future weather data typology on building energy performance–Investigating long-term patterns of climate change and extreme weather conditions. Appl. Energy 2019, 238, 696–720. [Google Scholar] [CrossRef]
  26. Gasper, R.; Blohm, A.; Ruth, M. Social and economic impacts of climate change on the urban environment. Curr. Opin. Environ. Sustain. 2011, 3, 150–157. [Google Scholar] [CrossRef]
  27. Guan, L.; Yang, J.; Bell, J.M. A method of preparing future hourly weather data for the study of the impact of global warming on built environment. In Proceedings of the QUT Research Week, Brisbane, Australia, 4–5 July 2005. [Google Scholar]
  28. Fiocchi, L.C. Matching building energy simulation results against measured data with weather file compensation factors. Ashrae Trans. 2014, 120, 397–405. [Google Scholar]
  29. Levermore, G.J.; Doylend, N.O. North American and European hourly-based weather data and methods for HVAC building energy anlayses and design by simulation/Discussion. Ashrae Trans. 2002, 108, 1053. [Google Scholar]
  30. Marion, W.; Urban, K. Users Manual for TMY2s: Derived from the 1961–1990 National Solar Radiation Data Base; National Renewable Energy Lab.: Golden, CO, USA, 1995.
  31. Wilcox, S.; Marion, W. Users Manual for TMY3 Data Sets; National Renewable Energy Laboratory: Golden, CO, USA, 2008.
  32. European Commission. Photovoltaic Geographical Information System. Available online: (accessed on 1 December 2018).
  33. Gazela, M.; Mathioulakis, E. A new method for typical weather data selection to evaluate long-term performance of solar energy systems. Sol. Energy 2001, 70, 339–348. [Google Scholar] [CrossRef]
  34. Kershaw, T.; Eames, M.; Coley, D. Comparison of multi-year and reference year building simulations. Build. Serv. Eng. Res. Technol. 2010, 31, 357–369. [Google Scholar] [CrossRef]
  35. Lundström, L. Weather Data for Building Simulation. 2012. Available online: (accessed on March 2019).
  36. Rahman, I.A.; Dewsbury, J. Selection of typical weather data (test reference years) for Subang, Malaysia. Build. Environ. 2007, 42, 3636–3641. [Google Scholar] [CrossRef]
  37. Crawley, D.B.; Lawrie, L.K. Rethinking the TMY: Is the ‘Typical’meteorological Year Best for Building Performance Simulation? In Proceedings of the BS2015, Hyderabad, India, 7–9 December 2015. [Google Scholar]
  38. Lee, K.; Yoo, H.; Levermore, G.J. Generation of typical weather data using the ISO Test Reference Year (TRY) method for major cities of South Korea. Build. Environ. 2010, 45, 956–963. [Google Scholar] [CrossRef]
  39. Lam, J.C.; Hui, S.C.M.; Chan, A.L.S. A statistical approach to the development of a typical meteorological year for Hong Kong. Archit. Sci. Rev. 1996, 39, 201–209. [Google Scholar] [CrossRef]
  40. Adelard, L.; Boyer, H.; Garde, F.; Gatina, J.C. A detailed weather data generator for building simulations. Energy Build. 2000, 31, 75–88. [Google Scholar] [CrossRef]
  41. Van Paassen, A.H.; Luo, Q.X. Weather data generator to study climate change on buildings. Build. Serv. Eng. Res. Technol. 2002, 23, 251–258. [Google Scholar] [CrossRef]
  42. Al-Salihi, A.M. Creation Typical Meteorological Year Data for Baghdad Province, Iraq. Iran. J. Energy Environ. 2014, 5, 78–86. [Google Scholar] [CrossRef]
  43. Zang, H.; Wang, M.; Huang, J.; Wei, Z.; Sun, G. A hybrid method for generation of typical meteorological years for different climates of China. Energies 2016, 9, 1094. [Google Scholar] [CrossRef]
  44. Hubbard, K.G.; Kunkel, K.E.; DeGaetano, A.T.; Redmond, K.T. Sources of Uncertainty in the Calculation of Design Weather Conditions. Ashrae Trans. 2005, 111, 317–326. [Google Scholar]
  45. Yassaghi, H.; Mostafavi, SN.; Hoque, S. Evaluation of current and future hourly weather data intended for building designs: A Philadelphia case study. Energy Build. 2019. Revision Submitted. [Google Scholar]
  46. Eames, M.; Kershaw, T.; Coley, D. On the creation of future probabilistic design weather years from UKCP09. Build. Serv. Eng. Res. Technol. 2011, 32, 127–142. [Google Scholar] [CrossRef]
  47. Chan, A.L.S. Developing future hourly weather files for studying the impact of climate change on building energy performance in Hong Kong. Energy Build. 2011, 43, 2860–2868. [Google Scholar] [CrossRef]
  48. Watkins, R.; Levermore, G.J.; Parkinson, J.B. The design reference year—A new approach to testing a building in more extreme weather using UKCP09 projections. Build. Serv. Eng. Res. Technol. 2013, 34, 165–176. [Google Scholar] [CrossRef]
  49. Jentsch, M.F.; James, P.A.B.; Bourikas, L.; Bahaj, A.S. Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates. Renew. Energy 2013, 55, 514–524. [Google Scholar] [CrossRef]
  50. Kikumoto, H.; Ooka, R.; Arima, Y.; Yamanaka, T. Study on the future weather data considering the global and local climate change for building energy simulation. Sustain. Cities Soc. 2015, 14, 404–413. [Google Scholar] [CrossRef]
  51. Zhu, M.; Pan, Y.; Huang, Z.; Xu, P. An alternative method to predict future weather data for building energy demand simulation under global climate change. Energy Build. 2016, 113, 74–86. [Google Scholar] [CrossRef]
  52. Chow, D.H.C.; Levermore, G.J. New algorithm for generating hourly temperature values using daily maximum, minimum and average values from climate models. Build. Serv. Eng. Res. Technol. 2007, 28, 237–248. [Google Scholar] [CrossRef]
  53. Guan, L. Preparation of future weather data to study the impact of climate change on buildings. Build. Environ. 2009, 44, 793–800. [Google Scholar] [CrossRef]
  54. Lee, T. Changing Climate: Ersatz Future Weather Data for Lifelong System Evaluation. In Proceedings of the Building Simulation, 12th Conference of International Building Performance Simulation Association, Sydney, Australia, 14–16 November 2011. [Google Scholar]
  55. Wang, X.; Chen, D.; Ren, Z. Assessment of climate change impact on residential building heating and cooling energy requirement in Australia. Build. Environ. 2010, 45, 1663–1682. [Google Scholar] [CrossRef]
  56. Watkins, R.; Levermore, G.J.; Parkinson, J.B. Constructing a future weather file for use in building simulation using UKCP09 projections. Build. Serv. Eng. Res. Technol. 2011, 32, 293–299. [Google Scholar] [CrossRef]
  57. Eames, M.; Kershaw, T.; Coley, D. A comparison of future weather created from morphed observed weather and created by a weather generator. Build. Environ. 2012, 56, 252–264. [Google Scholar] [CrossRef]
  58. Chen, D.; Wang, X.; Ren, Z. Selection of climatic variables and time scales for future weather preparation in building heating and cooling energy predictions. Energy Build. 2012, 51, 223–233. [Google Scholar] [CrossRef]
  59. Dickinson, R.; Brannon, B. Generating future weather files for resilience. In Proceedings of the International Conference on Passive and Low Energy Architecture, Los Angeles, CA, USA, 11–13 July 2016. [Google Scholar]
  60. Rastogi, P.; Andersen, M. Embedding Stochasticity in Building Simulation through Synthetic Weather Files. In Proceedings of the BS 2015, 14th International Conference of the International Building Performance Simulation Association, Hyderabad, India, 7–9 December 2015. [Google Scholar]
  61. Rastogi, P.; Andersen, M. Incorporating Climate Change Predictions in the Analysis of Weather-Based Uncertainty. In Proceedings of the SimBuild 2016, Salt Lake City, UT, USA, 8–12 August 2016. [Google Scholar]
  62. Nik, V.M. Making energy simulation easier for future climate—Synthesizing typical and extreme weather data sets out of regional climate models (RCMs). Appl. Energy 2016, 177, 204–226. [Google Scholar] [CrossRef]
  63. Belcher, S.E.; Hacker, J.N.; Powell, D.S. Constructing design weather data for future climates. Build. Serv. Eng. Res. Technol. 2005, 26, 49–61. [Google Scholar] [CrossRef]
  64. Bueno, B.; Norford, L.; Hidalgo, J.; Pigeon, G. The urban weather generator. J. Build. Perform. Simul. 2013, 6, 269–281. [Google Scholar] [CrossRef]
  65. Ivanov, V.Y.; Bras, R.L.; Curtis, D.C. A weather generator for hydrological, ecological, and agricultural applications. Water Resour. Res. 2007, 43. [Google Scholar] [CrossRef]
  66. Fatichi, S.; Ivanov, V.Y.; Caporali, E. Simulation of future climate scenarios with a weather generator. Adv. Water Resour. 2011, 34, 448–467. [Google Scholar] [CrossRef]
  67. Jentsch, M.F.; Bahaj, A.S.; James, P.A.B. Climate change future proofing of buildings—Generation and assessment of building simulation weather files. Energy Build. 2008, 40, 2148–2168. [Google Scholar] [CrossRef]
  68. ASHRAE. Chapter 14, Climatic Design Information; ASHRAE Handbook—Fundamentals: Atlanta, GA, USA, 2017. [Google Scholar]
  69. Yassaghi, H.; Hoque, S. Building Energy Demand Under Future Design Condition: A Case of Philadelphia Office Buildings. In Proceedings of the ASHRAE 2019 Buildings XIV International Conference, Clearwater Beach, FL, USA, 9–12 December 2019; Submitted. [Google Scholar]
  70. Hallegatte, S. Strategies to adapt to an uncertain climate change. Glob. Environ. Chang. 2009, 19, 240–247. [Google Scholar] [CrossRef]
  71. Lucon, O.; Ürge-Vorsatz, D.; Ahmed, A.Z.; Akbari, H.; Bertoldi Cabeza, L.; Eyre, N.; Gadgil, A.; Harvey, L.; Jiang, Y. Buildings, Climate Change 2014: Mitigation of Climate Change. In Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK, 2014. [Google Scholar]
  72. Georgiadou, M.C.; Hacking, T.; Guthrie, P. A conceptual framework for future-proofing the energy performance of buildings. Energy Policy 2012, 47, 145–155. [Google Scholar] [CrossRef]
  73. Kalogirou, S.A.; Bojic, M. Artificial neural networks for the prediction of the energy consumption of a passive solar building. Energy 2000, 25, 479–491. [Google Scholar] [CrossRef]
  74. Kajl, S.; Roberge, M.A.; Lamarche, L.; Malinowski, P. Evaluation of building energy consumption based on fuzzy logic and neural networks applications. In Proceedings of the CLIMA 2000 Conference, Brussels, Belgium, 20 August–2 September 1997. [Google Scholar]
  75. Li, K.; Su, H. Forecasting building energy consumption with hybrid genetic algorithm-hierarchical adaptive network-based fuzzy inference system. Energy Build. 2010, 42, 2070–2076. [Google Scholar] [CrossRef]
  76. Krarti, M. An overview of artificial intelligence-based methods for building energy systems. J. Sol. Energy Eng. 2003, 125, 331–342. [Google Scholar] [CrossRef]
  77. Naji, S.; Shamshirband, S.; Basser, H.; Keivani, A.; Alengaram, U.J.; Jumaat, M.Z.; Petković, D. Application of adaptive neuro-fuzzy methodology for estimating building energy consumption. Renew. Sustain. Energy Rev. 2016, 53, 1520–1528. [Google Scholar] [CrossRef]
  78. Zheng, G.; Jing, Y.; Huang, H.; Shi, G.; Zhang, X. Developing a fuzzy analytic hierarchical process model for building energy conservation assessment. Renew. Energy 2010, 35, 78–87. [Google Scholar] [CrossRef]
  79. Escandón, R.; Ascione, F.; Bianco, N.; Mauro, G.M.; Suárez, R.; Sendra, J.J. Thermal comfort prediction in a building category: Artificial neural network generation from calibrated models for a social housing stock in southern Europe. Appl. Therm. Eng. 2019, 150, 492–505. [Google Scholar] [CrossRef]
  80. Li, D.H.W.; Yang, L.; Lam, J.C. Impact of climate change on energy use in the built environment in different climate zones—A review. Energy 2012, 42, 103–112. [Google Scholar] [CrossRef]
  81. Energy Information Administration. Available online: (accessed on 15 November 2018).
  82. Crawley, D.B.; Hand, J.W.; Kummert, M.; Griffith, B.T. Contrasting the capabilities of building energy performance simulation programs. Build. Environ. 2008, 43, 661–673. [Google Scholar] [CrossRef]
  83. Guan, L. Energy use, indoor temperature and possible adaptation strategies for air-conditioned office buildings in face of global warming. Build. Environ. 2012, 55, 8–19. [Google Scholar] [CrossRef]
  84. Robert, A.; Kummert, M. Designing net-zero energy buildings for the future climate, not for the past. Build. Environ. 2012, 55, 150–158. [Google Scholar] [CrossRef]
  85. Chow, D.H.; Levermore, G.; Jones Lister, D.; Laycock, P.J.; Page, J. Extreme and near-extreme climate change data in relation to building and plant design. Build. Serv. Eng. Res. Technol. 2002, 23, 233–242. [Google Scholar] [CrossRef]
  86. Olonscheck, M.; Holsten, A.; Kropp, J.P. Heating and cooling energy demand and related emissions of the German residential building stock under climate change. Energy Policy 2011, 39, 4795–4806. [Google Scholar] [CrossRef]
  87. Frank, T. Climate change impacts on building heating and cooling energy demand in Switzerland. Energy Build. 2005, 37, 1175–1185. [Google Scholar] [CrossRef]
  88. Delfani, S.; Karami, M.; Pasdarshahri, H. The effects of climate change on energy consumption of cooling systems in Tehran. Energy Build. 2010, 42, 1952–1957. [Google Scholar] [CrossRef]
  89. Holmes, M.J.; Hacker, J.N. Climate change, thermal comfort and energy: Meeting the design challenges of the 21st century. Energy Build. 2007, 39, 802–814. [Google Scholar] [CrossRef]
  90. Wan, K.K.W.; Li, D.H.W.; Pan, W.; Lam, J.C. Impact of climate change on building energy use in different climate zones and mitigation and adaptation implications. Appl. Energy 2012, 97, 274–282. [Google Scholar] [CrossRef]
  91. Loveland, J.; Brown, G.Z. Impacts of Climate Change on the Energy Performance of Buildings in the United States; Offce of Technology Assessment, United Stated Congress: Washington, DC, USA, 1996.
  92. Wang, H.; Chen, Q. Impact of climate change heating and cooling energy use in buildings in the United States. Energy Build. 2014, 82, 428–436. [Google Scholar] [CrossRef]
  93. Fikru, M.G.; Gautier, L. The impact of weather variation on energy consumption in residential houses. Appl. Energy 2015, 144, 19–30. [Google Scholar] [CrossRef]
  94. Aguiar, R.; Oliveira, M.; Gonccedilalves, H. Climate change impacts on the thermal performance of Portuguese buildings. Results Siam Study. Build. Serv. Eng. Res. Technol. 2002, 23, 223–231. [Google Scholar] [CrossRef]
  95. Chow, D.H.C.; Levermore, G.J. The effects of future climate change on heating and cooling demands in office buildings in the UK. Build. Serv. Eng. Res. Technol. 2010, 31, 307–323. [Google Scholar] [CrossRef]
  96. Ouedraogo, B.I.; Levermore, G.J.; Parkinson, J.B. Future energy demand for public buildings in the context of climate change for Burkina Faso. Build. Environ. 2012, 49, 270–282. [Google Scholar] [CrossRef]
  97. Jankovic, L. Designing Resilience of the Built Environment to Extreme Weather Events. Sustainability 2018, 10, 141. [Google Scholar] [CrossRef]
  98. Coley, D.; Kershaw, T.; Eames, M. A comparison of structural and behavioural adaptations to future proofing buildings against higher temperatures. Build. Environ. 2012, 55, 159–166. [Google Scholar] [CrossRef]
  99. Wan, K.K.W.; Li, D.H.W.; Lam, J.C. Assessment of climate change impact on building energy use and mitigation measures in subtropical climates. Energy 2011, 36, 1404–1414. [Google Scholar] [CrossRef]
  100. Coley, D.; Kershaw, T. Changes in internal temperatures within the built environment as a response to a changing climate. Build. Environ. 2010, 45, 89–93. [Google Scholar] [CrossRef]
  101. Jenkins, D.P.; Patidar, S.; Banfill, P.F.G.; Gibson, G.J. Probabilistic climate projections with dynamic building simulation: Predicting overheating in dwellings. Energy Build. 2011, 43, 1723–1731. [Google Scholar] [CrossRef]
  102. Jenkins, D.P.; Ingram, V.; Simpson, S.A.; Patidar, S. Methods for assessing domestic overheating for future building regulation compliance. Energy Policy 2013, 56, 684–692. [Google Scholar] [CrossRef]
  103. Taylor, J.; Davies, M.; Mavrogianni, A.; Chalabi, Z.; Biddulph, P.; Oikonomou, E.; Das, P.; Jones, B. The relative importance of input weather data for indoor overheating risk assessment in dwellings. Build. Environ. 2014, 76, 81–91. [Google Scholar] [CrossRef]
  104. Nik, V.M.; Arfvidsson, J. Using typical and extreme weather files for impact assessment of climate change on buildings. Energy Procedia 2017, 132, 616–621. [Google Scholar] [CrossRef]
  105. Shen, P. Impacts of climate change on US building energy use by using downscaled hourly future weather data. Energy Build. 2017, 134, 61–70. [Google Scholar] [CrossRef]
  106. Dirks, J.A.; Gorrissen, W.J.; Hathaway, J.H.; Skorski, D.C.; Scott, M.J.; Pulsipher, T.C.; Huang, M.; Liu, Y.; Rice, J.S. Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach. Energy 2015, 79, 20–32. [Google Scholar] [CrossRef]
  107. Flores-Larsen, S.; Filippín, C.; Barea, G. Impact of climate change on energy use and bioclimatic design of residential buildings in the 21st century in Argentina. Energy Build. 2019, 184, 216–229. [Google Scholar] [CrossRef]
  108. Cellura, M.; Guarino, F.; Longo, S.; Tumminia, G. Climate change and the building sector: Modelling and energy implications to an office building in southern Europe. Energy Sustain. Dev. 2018, 45, 46–65. [Google Scholar] [CrossRef]
  109. Arima, Y.; Ooka, R.; Kikumoto, H.; Yamanaka, T. Effect of climate change on building cooling loads in Tokyo in the summers of the 2030s using dynamically downscaled GCM data. Energy Build. 2016, 114, 123–129. [Google Scholar] [CrossRef]
  110. Zhao, D.; Fan, H.; Pan, L.; Xu, Q.; Zhang, X. Energy Consumption Performance Considering Climate Change in Office Building. Procedia Eng. 2017, 205, 3448–3455. [Google Scholar] [CrossRef]
  111. Karimpour, M.; Belusko, M.; Xing, K.; Boland, J.; Bruno, F. Impact of climate change on the design of energy efficient residential building envelopes. Energy Build. 2015, 87, 142–154. [Google Scholar] [CrossRef]
  112. Rosenzweig, C.; Solecki, W.D.; Romero-Lankao, P.; Mehrotra, S.; Dhakal, S.; Ibrahim, S.A. Climate Change and Cities: Second Assessment Report of the Urban Climate Change Research Network; Cambridge University Press: Cambridge, UK, 2018. [Google Scholar]
  113. Yassaghi, H.; Hoque, S. Climate change impacts on office buildings performance: A case study of Philadelphia USA, in Buildings, Cities and Performance. In Proceedings of the Prometheus, the Journal of IIT PhD Program 2018; IIT: Chicago, IL, USA, 2018. [Google Scholar]
  114. Roaf, S.; Crichton, D.; Nicol, F. Adapting Buildings and Cities for Climate Change: A 21st Century Survival Guide; Routledge: London, UK, 2009. [Google Scholar]
  115. Kwok, A.G.; Rajkovich, N.B. Addressing climate change in comfort standards. Build. Environ. 2010, 45, 18–22. [Google Scholar] [CrossRef]
  116. Wang, L.; Mathew, P.; Pang, X. Uncertainties in energy consumption introduced by building operations and weather for a medium-size office building. Energy Build. 2012, 53, 152–158. [Google Scholar] [CrossRef]
  117. Shibuya, T.; Croxford, B. The effect of climate change on office building energy consumption in Japan. Energy Build. 2016, 117, 149–159. [Google Scholar] [CrossRef]
  118. Lomas, K.J.; Ji, Y. Resilience of naturally ventilated buildings to climate change: Advanced natural ventilation and hospital wards. Energy Build. 2009, 41, 629–653. [Google Scholar] [CrossRef]
  119. De Wilde, P.; Tian, W. Management of thermal performance risks in buildings subject to climate change. Build. Environ. 2012, 55, 167–177. [Google Scholar] [CrossRef]
  120. Wang, L.; Liu, X.; Brown, H. Prediction of the impacts of climate change on energy consumption for a medium-size office building with two climate models. Energy Build. 2017, 157, 218–226. [Google Scholar] [CrossRef]
  121. Tronchin, L.; Fabbri, K.; Bertolli, C. Controlled Mechanical Ventilation in Buildings: A Comparison between Energy Use and Primary Energy among Twenty Different Devices. Energies 2018, 11, 2123. [Google Scholar] [CrossRef]
  122. Wang, Y.; Berardi, U.; Akbari, H. Comparing the effects of urban heat island mitigation strategies for Toronto, Canada. Energy Build. 2016, 114, 2–19. [Google Scholar] [CrossRef]
  123. Silva, A.S.; Ghisi, E. Uncertainty analysis of user behaviour and physical parameters in residential building performance simulation. Energy Build. 2014, 76, 381–391. [Google Scholar] [CrossRef]
  124. Zani, A.; Tagliabue, L.C.; Poli, T.; Ciribini, A.L.; de Angelis, E.; Manfren, M. Occupancy profile variation analyzed through generative modelling to control building energy behavior. Procedia Eng. 2017, 180, 1495–1505. [Google Scholar] [CrossRef]
  125. Basso, G.L.; Nastasi, B.; Salata, F.; Golasi, I. Energy retrofitting of residential buildings—How to couple Combined Heat and Power (CHP) and Heat Pump (HP) for thermal management and off-design operation. Energy Build. 2017, 151, 293–305. [Google Scholar] [CrossRef]
  126. Huang, J.; Gurney, K.R. Impact of climate change on US building energy demand: Sensitivity to spatiotemporal scales, balance point temperature, and population distribution. Clim. Chang. 2016, 137, 171–185. [Google Scholar] [CrossRef]
  127. Morton, T.A.; Bretschneider, P.; Coley, D.; Kershaw, T. Building a better future: An exploration of beliefs about climate change and perceived need for adaptation within the building industry. Build. Environ. 2011, 46, 1151–1158. [Google Scholar] [CrossRef]
  128. Vermaak, H. Planning Deep Change through a Series of Small Wins; Academy of Management: Briarcliff Manor, NY, USA, 2013. [Google Scholar]
  129. Levy, A.; Merry, U. Organizational Transformation: Approaches, Strategies, Theories; Greenwood Publishing Group: Westport, CT, USA, 1986. [Google Scholar]
  130. Blaikie, P.; Cannon, T.; Davis, I.; Wisner, B. At Risk: Natural Hazards, People’s Vulnerability and Disasters; Routledge: London, UK, 2005. [Google Scholar]
  131. Dang, H.H.; Michaelowa, A.; Tuan, D.D. Synergy of adaptation and mitigation strategies in the context of sustainable development: The case of Vietnam. Clim. Policy 2003, 3, S81–S96. [Google Scholar] [CrossRef]
  132. Hopfe, C.J.; Hensen, J.L.M. Uncertainty analysis in building performance simulation for design support. Energy Build. 2011, 43, 2798–2805. [Google Scholar] [CrossRef]
  133. de Wilde, P.; Rafiq, Y.; Beck, M. Uncertainties in predicting the impact of climate change on thermal performance of domestic buildings in the UK. Build. Serv. Eng. Res. Technol. 2008, 29, 7–26. [Google Scholar] [CrossRef]
  134. Kershaw, T.; Eames, M.; Coley, D. Assessing the risk of climate change for buildings: A comparison between multi-year and probabilistic reference year simulations. Build. Environ. 2011, 46, 1303–1308. [Google Scholar] [CrossRef]
  135. Hawkins, E.; Sutton, R. The potential to narrow uncertainty in regional climate predictions. Bull. Am. Meteorol. Soc. 2009, 90, 1095–1108. [Google Scholar] [CrossRef]
  136. Yip, S.; Ferro, C.A.T.; Stephenson, D.B.; Hawkins, E. A simple, coherent framework for partitioning uncertainty in climate predictions. J. Clim. 2011, 24, 4634–4643. [Google Scholar] [CrossRef]
  137. Northrop, P.J.; Chandler, R.E. Quantifying sources of uncertainty in projections of future climate. J. Clim. 2014, 27, 8793–8808. [Google Scholar] [CrossRef]
  138. Hawkins, E.; Sutton, R. The potential to narrow uncertainty in projections of regional precipitation change. Clim. Dyn. 2011, 37, 407–418. [Google Scholar] [CrossRef]
  139. San-Martín, D.; Manzanas, R.; Brands, S.; Herrera, S.; Gutiérrez, J.M. Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods. J. Clim. 2017, 30, 203–223. [Google Scholar] [CrossRef]
  140. Hayes, K. Uncertainty and Uncertainty Analysis Methods; CSIRO: Canberra, Australia, 2011. [Google Scholar]
  141. Goodess, C.M.; Hanson, C.; Hulme, M.; Osborn, T.J. Representing Climate and Extreme Weather Events in Integrated Assessment Models: A Review of Existing Methods and Options for Development. Integr. Assess. 2003, 4, 145–171. [Google Scholar] [CrossRef]
  142. Chinazzo, G.; Rastogi, P.; Andersen, M. Assessing robustness regarding weather uncertainties for energy-efficiency-driven building refurbishments. Energy Procedia 2015, 78, 931–936. [Google Scholar] [CrossRef]
  143. Hammonds, J.S.; Hoffman, F.O.; Bartell, S.M. An Introductory Guide to Uncertainty Analysis in Environmental and Health Risk Assessment; US DOE: Washington, DC, USA, 1994.
  144. Cecconi, F.R.; Manfren, M.; Tagliabue, L.C.; Ciribini, A.L.C.; de Angelis, E. Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach. Energy Build. 2017, 148, 128–141. [Google Scholar] [CrossRef]
  145. Farah, S.; Whaley, D.; Saman, W.; Boland, J. Integrating climate change into meteorological weather data for building energy simulation. Energy Build. 2019, 183, 749–760. [Google Scholar] [CrossRef]
  146. Hu, M.; Qiu, Y. A comparison of building energy codes and policies in the USA, Germany, and China: Progress toward the net-zero building goal in three countries. Clean Technol. Environ. Policy 2019, 21, 291–305. [Google Scholar] [CrossRef]
  147. Sánchez-García, D.; Rubio-Bellido, C.; del Río, J.J.M.; Pérez-Fargallo, A. Towards the quantification of energy demand and consumption through the adaptive comfort approach in mixed mode office buildings considering climate change. Energy Build. 2019, 187, 173–185. [Google Scholar] [CrossRef]
  148. Mata, É.; Wanemark, J.; Nik, V.M.; Kalagasidis, A.S. Economic feasibility of building retrofitting mitigation potentials: Climate change uncertainties for Swedish cities. Appl. Energy 2019, 242, 1022–1035. [Google Scholar] [CrossRef]
  149. Shen, P.; Braham, W.; Yi, Y. The feasibility and importance of considering climate change impacts in building retrofit analysis. Appl. Energy 2019, 233, 254–270. [Google Scholar] [CrossRef]
  150. Shen, P.; Braham, W.; Yi, Y.; Eaton, E. Rapid multi-objective optimization with multi-year future weather condition and decision-making support for building retrofit. Energy 2019, 172, 892–912. [Google Scholar] [CrossRef]
Figure 1. Global land temperature anomalies 1880–2018 [16].
Figure 1. Global land temperature anomalies 1880–2018 [16].
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Figure 2. Yearly mean temperatures for different Greenhouse Gas (GHG) emissions scenarios [22].
Figure 2. Yearly mean temperatures for different Greenhouse Gas (GHG) emissions scenarios [22].
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Figure 3. Approach to assess building energy demand and consumption using DD and TMY files.
Figure 3. Approach to assess building energy demand and consumption using DD and TMY files.
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Figure 4. Summary of uncertainties in conducting building performance assessment.
Figure 4. Summary of uncertainties in conducting building performance assessment.
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Figure 5. Sample probabilistic approach to propagate building energy uncertainties.
Figure 5. Sample probabilistic approach to propagate building energy uncertainties.
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Table 1. Selected literature on creation and modification of Typical Years (TYs).
Table 1. Selected literature on creation and modification of Typical Years (TYs).
RefPropositionMain finding(s)Location
[36]The effect of using different weight factorsEqual weight factors have the best correlation among ranks.Malaysia
[37]Proposed a new extreme meteorological yearA combination of more than one typical and two extreme years are a suitable fit to building analysis.USA
[38]Using cloud cover as an alternative for daily solar radiationGood agreement between the long-term values of conventional method and the proposed.South Korea
[39]Developed a typical meteorological year for Hong KongGood agreement between long term observation and TMY file developed for building consumption.Hong Kong
[40]RUNEOLE typical weather toolDeveloped a new code to create weather sequences for building applications.Global
[41]New weather generatorA weather generator independent of the location and can be adapted to local climate change.Global
[42]Created TMY for IraqCreated TMY for the location of Iraq using the FS method.Iraq
[43]Combined the Danish method and Festa–Ratto methodThe combination of methods showed better long-term average data.China
[34]Compared DSY and TRY within buildingTRY is not accurate to derive indications of the average energy use and DSY tends to underestimate level of overheating.UK
Table 2. Summary of literature on generation and modification of future hourly weather files for building applications.
Table 2. Summary of literature on generation and modification of future hourly weather files for building applications.
[27]Modified the TRY by increasing temperature steadily for each season. Air humidity was calculated using the psychometric chart and by assuming relative humidity will face no changes.Australia
[46]Presented a method for creation of future probabilistic years.UK
[47]Merged GCMs output of projected monthly parameters under two emission scenarios for three periods to a TMY file using the morphing procedure.Hong Kong
[48]Proposed a new DRY as a substitute for DSY that could suitably account for extreme weather conditions for both summer and winter.UK
[49]Compared future weather data produced by the output from the RCM and morphed data from GCM.UK
[50]Physically downscaled the results from the GCM to predict local future climate.Japan
[51]Presented a method to develop future hourly data based on long-term regional and short-term observations using the morphing technique.China
[52]Presented a new algorithm for the creation of hourly temperature data for the UK called the Quarter Sin Method, which uses daily temperature parameters.UK
[53]Reviewed weather generating methods of extrapolating, imposed offset, stochastic, and global climate models, and presented a comprehensive framework to generate future hourly weather data.Australia
[54]Discusses how Ersatz Future Metrological Year (EFMY) climate files are created.Australia
[55]Applied the morphing process to weather data prepared by the OZClim simulation tool.Australia
[56]Presented a method to construct hourly weather file for temperature, relative humidity, cloud cover, and solar radiation from the UKCP09 data.UK
[57]Cloud Radiation Model (CRM) was proposed as an alternative method when using the weather generator. In addition, suggested using only a shift for mean temperatures when using the morphing technique.UK
[58]Used the TMYs extracted from Accurate and applied the morphing procedure based on the predictions of three GCMs for temperatures increasing from 0-6 with 0.5 intervals.Australia
[59]Introduced a new weather generator which produces Energy Plus Weather (EPW) and TMY files projected to several future time slices for two IPCC AR5 emission scenario.US
[60,61]Developed a new weather generator that produces synthetic weather time series for the US and any location worldwide based on the IPCC AR5.US
[62]Presented a method to synthesize weather data derived from RCMs.Sweden
[63]Developed a technique called “morphing” to create future hourly weather data.Global
Table 3. Summary of research on building energy performance under future climate conditions.
Table 3. Summary of research on building energy performance under future climate conditions.
[83]DOE-2.1EOfficesIncrease of 0.4–15% in energy use by 2070 and overheating increase for an outdoor temperature increase of more than 2°C.Australia
[84]TRNSYSNZEBDue to increased cooling loads, the target of a Net Zero Energy Building (NZEB) cannot be attained for most future years.Montreal
[85]EnergyPlusOffice & ResidentialIncrease of up to 20% in cooling requirements.Hong Kong
[86]DIN with a degree day approachResidentialDepending on renovated factors, climate scenarios and demographic changes, by 2060 cooling demand will remain low unless the amount of A/Cs increase.Germany
[87]HELIOSResidential & commercialThermal insulation level will have a critical impact on heating energy demand.Zurich
[88]Temperature interval (bin)Cooling applicationsDirect evaporative cooling is incapable of providing thermal comfort in the future and indirect–direct evaporative cooling would be inefficient. Tehran
[89]ENERGY2OfficesHigh thermal mass buildings can provide better comfort conditions while considering sustainability. London
[90]VisualDOE4.1OfficesA shift towards more electrical cooling consumption, which would lead to higher emissions, is anticipated. In addition, a 1–2°C increase in Set Point Temperature (SPT) has the potential to mitigate GHG emissions.China
[91]CALPAS3Commercial & residentialThe increase in annual cooling overcomes the decrease in heating.US
[55]AccuRateResidentialBuildings face a heating and cooling requirement change from 48% to 350%, depending on the location under study.Australia
[92]EnergyPlusResidential & CommercialThe impact of climate change varies greatly depending on the location and the structure of the building. In addition, in the future natural ventilation effectiveness would considerably decline in hot regions. US
[93]Degree minuteResidential & NZEBNet-zero energy buildings are less sensitive than code-current buildings towards climate variables. US
[94]ESP-rResidential & CommercialNo significant relation was found using top-down approach between weather and energy consumption, but the bottom-up approach showed a decrease in heating loads and an increase in cooling loads. Portugal
[95]Second-order modelOfficesNatural ventilation would not be enough for cooling requirements; the decrease in heating requirements compensated the increase in cooling demands; building orientation and thermal mass of building are significant.UK
[96]IES VE, MacroFlo, SunCastPublicPossible increase in annual energy consumption of 99% by end of century.Burkina Faso
[97]EnergyPlusResidentialProposed a resilient design for local areas.UK
[98]-School & residentialBehavioral adaptations are as effective as physical/architectural changes to combat overheating. UK
[99]DOE4.1OfficesExternal thermal insulation of walls would not be effective. Better options are lowering solar heat gain through windows, lowering Light Load Density (LLD) and improving the COP of the chiller. Hong Kong
[100]IESResidential & CommercialA linear relation between indoor and outdoor temperatures was found. Solar heat gains play a crucial role in thermal comfort. UK
[101]ESP-r-Proposed a regression method to relate climate variables with the internal temperatures. UK
[102]SAP, RdSAP, IES-VEResidentialAssessed overheating using different simulating tools; different overheating methodologies can produce significantly different outputs; a Low Carbon Futures (LCF) probabilistic approach was presented. UK
[27]DOE-2.1EOfficesSolar radiation has the most effect on building energy performance.Australia
[47]EnergyplusOffice & ResidentialIncreased energy consumption is expected, compared to the baseline weather data. Hong Kong
[103]EnergyplusResidentialOverheating studies need to consider the variability of building performance under regional weather variations. UK
[104]MatlabResidentialUsing the downscaled weather data from [62] results for heating and cooling showed good agreement with the results from the original weather data with the advantage of accounting for extreme conditions.Sweden
[50]TRNSYSResidentialThe sensible heat load was predicted to increase by 15%. Tokyo
[51]EnergyplusCommercialThe largest percentage increase of whole-building energy demand for an office building, hotel, and shopping mall are respectively 2.6%, 3.1%, and 1.4% by the 21st century.China
[105]EnergyPlusResidential & OfficeTotal annual energy consumption range from -3.2% to 14% under the A2 scenario in different regions; however, growing peak electricity poses great risk to future grid.US
[106]Building ENergy Demand (BEND)Commercial & ResidentialPresented numerous results on the impact of climate change on peak energy demand over eastern interconnection locations in the USUS
[25]EnergyPlusCommercial & ResidentialApplied dynamically and statistically downscaled weather data to building prototypes and highlighted the importance of considering extreme conditionsCity of Geneva
[107]EnergyPlusMid-income houseHeating and cooling requirements will be up to 59% lower and 790% higher respectively. Sun shading was found to be an effective response to the warming climate.Argentina
[108]TRNSYSOfficeAn overall increase in energy consumption in a range of 50–119% increase with a relative decrease in heating and increase in cooling.Europe
[109]TRNSYSTwo-story detached houseA 26% increase in total sensible heat load and 10% increase in latent heat load is expected by the near future.Tokyo
[110]DesignBuilderOfficeThe impact of the warming climate to the case study is insignificantShanghai
[111]AccuRateResidentialClimate change shifts the dominant heating requirement to a more cooling demand and measure to reduce cooling loads become criticalAdelaide
Table 4. Summary of adaptation and mitigation responses to the changing climate.
Table 4. Summary of adaptation and mitigation responses to the changing climate.
Thermal insulation/capacity [87,89,90,91,98,99,100,117]Shading[89,90,91,98]
Natural Ventilation (NV)[89,95,118]Solar control glass[98]
Window and Wall U-Value[90,95,98]Night ventilation[89,98]
Window to Wall Ratio (WWR)[90,119]Window opening[98,100]
Light and/or plug-in equipment [90,91,98,116,117,119]Earlier day schedules[98]
Chiller Coefficient of Performance (COP)[90,98,99,119]Setpoint Temperature (ST)[90,98,116,117,120]
Orientation [95,100]Adaptive behaviors [115]
Building size[100]Clothing standards[100]
Infiltration rate[100,119]Night setback [116]
HVAC operations[116,120]Metabolic rate[119]
Controlled ventilation [116,120,121]Overhangs[117]
Solar Heat Gain Coefficient (SHGC)[119]Cool roof[122]
Equipment Efficiency (EE)[119]User behavior[123,124]
Combined technologies[125]Population distribution[126]

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