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Special Issue "Building Energy Use: Modeling and Analysis"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Sustainable Energy".

Deadline for manuscript submissions: 31 October 2018

Special Issue Editors

Guest Editor
Prof. Dr. Yuyu Zhou

Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA 50011, USA
Website | E-Mail
Phone: +1-515-2942842
Interests: building energy use; fossil-fuel emissions; urbanization; urban heat island; nighttime lights remote sensing; renewable energy; climate change
Guest Editor
Prof. Dr. Yi Jiang

Director of Building Energy Research Centre, Co-Director of Tsinghua-UPENN Centre for Building Simulation and Energy Study, Director of Tsinghua UTC Joined Lab, Tsinghua University, Beijing, China
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Interests: building energy studies
Guest Editor
Dr. Sha Yu

Joint Global Change Research Institute, Pacific Northwest National Lab, College Park, MD 20740, USA
Website | E-Mail
Interests: integrated assessment modeling in building sector; building energy efficiency; clean energy policy; energy planning; building codes
Guest Editor
Prof. Dr. Diana Ürge-Vorsatz

Director, Center for Climate Change and Sustainable Energy Policy (3CSEP), Central European University, Budapest, Hungary
Website | E-Mail
Interests: engineering economic modeling; integration of climate change mitigation and sustainable development objectives; energy efficient and sustainable buildings; climate change mitigation in cities

Special Issue Information

Dear Colleagues,

The building sector accounts for about one-third of global final energy demands. It contributes approximately one-third of energy-related CO2 emissions in the global carbon cycle. It plays an important role in air pollution by emitting black carbon. The building sector is also susceptible to climate feedbacks (e.g., potential climate change and urban heat island) because of its significant demand for space heating and cooling energy. More important, it was found that the global building energy demand will increase in the future and will at least double by 2050. Improved understanding of building energy use, influencing factors, and associated environmental impacts is highly needed and essential to developing strategies for meeting challenges of growing energy demand and environmental sustainability.

This Special Issue aims to publish original manuscripts of innovative research in building energy use modeling and analysis. Comprehensive reviews of this research field are also welcome. The potential topics include, but are not limited to:

  • Building energy modeling (e.g., EnergyPlus and Integrated Assessment) from single building to global levels
  • Comparison of building energy modeling techniques
  • Validation of building energy modeling using new data (e.g., smart meter)
  • Impacts of global changes (e.g., climate, urban heat island, and extreme heat events) on building energy use
  • Impacts of human activities (e.g., behavior and building operation) on building energy use
  • Impacts of technology advancement and policy on building energy use
  • Building CO2, black carbon, and HFC emissions modeling and analysis
  • Policy analysis and implications (e.g., zero energy building) for building energy use
  • Impacts of building energy use on the environment (e.g., air pollution)
  • Application of big data in building energy use modeling and analysis
Prof. Dr. Yuyu Zhou
Prof. Dr. Yi Jiang
Dr. Sha Yu
Prof. Dr. Diana Ürge-Vorsatz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • building energy
  • building emissions
  • integrated assessment modeling
  • model comparison
  • model validation
  • climate change
  • urban heat island
  • building policy
  • sustainable buildings
  • big data

Published Papers (22 papers)

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Research

Open AccessArticle Application of Wind as a Renewable Energy Source for Passive Cooling through Windcatchers Integrated with Wing Walls
Energies 2018, 11(10), 2536; https://doi.org/10.3390/en11102536
Received: 16 July 2018 / Revised: 24 August 2018 / Accepted: 23 September 2018 / Published: 23 September 2018
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Abstract
Generally, two-third of a building’s energy is consumed by heating, ventilation and air-conditioning systems. One green alternative for conventional air conditioner systems is the implementation of passive cooling. Wing walls and windcatchers are two prominent passive cooling techniques which use wind as a
[...] Read more.
Generally, two-third of a building’s energy is consumed by heating, ventilation and air-conditioning systems. One green alternative for conventional air conditioner systems is the implementation of passive cooling. Wing walls and windcatchers are two prominent passive cooling techniques which use wind as a renewable resource for cooling. However, in low wind speed regions and climates, the utilization of natural ventilation systems is accompanied by serious uncertainties. The performance of ventilation systems can be potentially enhanced by integrating windcatchers with wing walls. Since previous studies have not considered this integration, in the first part of this research the effect of this integration on the ventilation performance was assessed and the optimum angle was revealed. However, there is still gap of this combination; thus, in the second part, the impact of wing wall length on the indoor air quality factors was evaluated. This research implemented a Computational Fluid Dynamics (CFD) method to address the gap. The CFD simulation was successfully validated with experimental data from wind tunnel tests related to the previous part. Ten different lengths from 10 cm to 100 cm were analyzed and it was found that the increase in wing wall length leads to a gradual reduction in ventilation performance. Hence, the length does not have a considerable influence on the indoor air quality factors. However, the best performance was seen in 10 cm, that could provide 0.8 m/s for supply air velocity, 790 L/s for air flow rate, 39.5 1/h for air change rate, 107 s for mean age of air and 92% for air change effectiveness. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance
Energies 2018, 11(9), 2373; https://doi.org/10.3390/en11092373
Received: 6 August 2018 / Revised: 6 September 2018 / Accepted: 6 September 2018 / Published: 8 September 2018
PDF Full-text (845 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation
[...] Read more.
A new model predictive control (MPC) algorithm is used to select optimal air conditioning setpoints for a commercial office building, considering variable electricity prices, weather and occupancy. This algorithm, Cost-Comfort Particle Swarm Optimization (CCPSO), is the first to combine a realistic, smooth representation of occupants’ willingness to pay for thermal comfort with a bottom-up, nonlinear model of the building and air conditioning system under control. We find that using a quadratic preference function for temperature can yield solutions that are both more comfortable and lower-cost than previous work that used a “brick wall” preference function with no preference for further cooling within an allowed temperature band and infinite aversion to going outside the allowed band. Using historical pricing data for a summer month in Chicago, CCPSO provided a 1% reduction in costs vs. a similar “brick-wall” MPC approach with the same comfort and 6–11% reduction in costs vs. other control strategies in the literature. CCPSO can also be used to operate the building with much greater comfort and costs or much lower costs and comfort than the “brick-wall” approach, depending on user preferences. CCPSO also reduced peak-hours demand by 3% vs. the “brick-wall” strategy and 4–14% vs. other strategies. At the same time, the CCPSO strategy increased off-peak energy consumption by 15% or more vs. other control methods. This may be valuable for power systems integrating large amounts of renewable power, which can otherwise become uneconomic due to saturation of demand during off-peak hours. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Theoretical and Experimental Contributions on the Use of Smart Composite Materials in the Construction of Civil Buildings with Low Energy Consumption
Energies 2018, 11(9), 2310; https://doi.org/10.3390/en11092310
Received: 24 July 2018 / Revised: 17 August 2018 / Accepted: 30 August 2018 / Published: 2 September 2018
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Abstract
The paper presents the theoretical and experimental studies undertaken for the realization of an intelligent composite material with phase shift that has optimal characteristics in the thermal energy storage process and an experimental method for integrating the material with phase change in a
[...] Read more.
The paper presents the theoretical and experimental studies undertaken for the realization of an intelligent composite material with phase shift that has optimal characteristics in the thermal energy storage process and an experimental method for integrating the material with phase change in a possible efficient system to be used in the construction of a dwelling. It analyzes the main factors in designing such systems (the temperature limits between which the system must operate, the melting/solidification temperature of the Phase Change Material (PCM), the latent heat of the PCM, the degree of thermal loading, the bed configuration of PCM capsules and a PCM-RB01 material is set. A micro-encapsulation method was chosen and a “solar wall” is made where the incident solar radiation is absorbed by the PCM embedded in the wall, so the stored heat is used for heating and ventilation of a home. Experimental research has shown that developed PCM allows a maximum room temperature reduction of about 4 °C during the day and can reduce the night-time heating load. Also, despite the lower thermal energy absorption capacity, the developed PCM-RB01 material provides a superior physical stability compared to the classical types of integration. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Evaluation of Energy Consumption in German Hospitals: Benchmarking in the Public Sector
Energies 2018, 11(9), 2279; https://doi.org/10.3390/en11092279
Received: 20 June 2018 / Revised: 7 August 2018 / Accepted: 28 August 2018 / Published: 30 August 2018
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Abstract
The use of benchmarking in the management of healthcare facilities enables immediate comparison between hospitals. Benchmarking allows ascertaining their expected energy consumption and estimating the possible savings margin. In the 2005–2015 period, 90 EU Eco-Audits of 23 public hospitals in Germany were studied
[...] Read more.
The use of benchmarking in the management of healthcare facilities enables immediate comparison between hospitals. Benchmarking allows ascertaining their expected energy consumption and estimating the possible savings margin. In the 2005–2015 period, 90 EU Eco-Audits of 23 public hospitals in Germany were studied to analyze the energy consumption related with weather conditions, built surface area, gross domestic product (GDP), geographic location (GL), bed numbers, and employee numbers. The results reveal that the average annual energy consumption of a hospital under normal conditions, both climatic and operational, is 0.27 MWh/m2, 14.37 MWh/worker, and 23.41 MWh/bed. The indicator dependent on the number of beds proved to be the most suitable as a reference to quantify the energy consumption of a hospital. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Thermal 3D CFD Simulation with Active Transparent Façade in Buildings
Energies 2018, 11(9), 2265; https://doi.org/10.3390/en11092265
Received: 12 July 2018 / Revised: 13 August 2018 / Accepted: 20 August 2018 / Published: 28 August 2018
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Abstract
In recent years active façades have acquired greater importance given their capacity to improve the energy efficiency of buildings. One such type is the so-called Active Transparent Façade (ATF). A 3D numerical model based on computational fluid dynamics (CFD) and the Finite Element
[...] Read more.
In recent years active façades have acquired greater importance given their capacity to improve the energy efficiency of buildings. One such type is the so-called Active Transparent Façade (ATF). A 3D numerical model based on computational fluid dynamics (CFD) and the Finite Element Method (FEM) has been generated to simulate the thermal performance of buildings equipped with this type of façade. This model is introduced for general application and allows the design parameters to be adapted for this system. The case study of Le Corbusier’s proposal for the City of Refuge in Paris, the clearest example of previous use of an ATF is examined. In addition, a proposal is presented for the energy improvement of Le Corbusier’s original solution. In order to do so, the conditions for the supply of air into the ATF cavity and in the mechanical ventilation system are assessed to guarantee comfort conditions. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Deducing the Optimal Control Method for Electrochromic Triple Glazing through an Integrated Evaluation of Building Energy and Daylight Performance
Energies 2018, 11(9), 2205; https://doi.org/10.3390/en11092205
Received: 16 July 2018 / Revised: 20 August 2018 / Accepted: 20 August 2018 / Published: 23 August 2018
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Abstract
Electrochromic glass is anticipated as the next generation of solar control glass for construction because it can control the transmittance of the glass itself. This study analyzed building energy and light environment performance by applying electrochromic glass in triple glazing in order to
[...] Read more.
Electrochromic glass is anticipated as the next generation of solar control glass for construction because it can control the transmittance of the glass itself. This study analyzed building energy and light environment performance by applying electrochromic glass in triple glazing in order to verify both the solar control characteristics of electrochromic glass and its high insulation performance. This paper evaluates the performance of the electrochromic glass developed by our research team in Korea in five control conditions of varying temperatures and solar radiation levels. By analyzing the cooling and heating load, lighting energy, Daylight Glare Index (DGI), and interior illuminance when applying the selected conditions to office buildings, this paper discerns the optimal control conditions for electrochromic glass. To do so, the optical characteristic data of the electrochromic glass was analyzed via an experiment, and the creation of triple glazing for construction was conducted. The performance of electrochromic glass was evaluated by analyzing hourly and yearly data for cooling, heating load, and lighting energy during a typical day in summer and winter. From this analysis, the control condition with an outstanding performance from an energy perspective was identified. The performance of the light environment was assessed, and the EDPI overall evaluation index was used to find the electrochromic glass’ optimum control conditions for integrating energy and light environment. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Experimental Study of a Bubble Mode Absorption with an Inner Vapor Distributor in a Plate Heat Exchanger-Type Absorber with NH3-LiNO3
Energies 2018, 11(8), 2137; https://doi.org/10.3390/en11082137
Received: 16 July 2018 / Revised: 7 August 2018 / Accepted: 10 August 2018 / Published: 16 August 2018
Cited by 1 | PDF Full-text (6940 KB) | HTML Full-text | XML Full-text
Abstract
Absorption systems are a sustainable solution as solar driven air conditioning devices in places with warm climatic conditions, however, the reliability of these systems must be improved. The absorbing component has a significant effect on the cycle performance, as this process is complex
[...] Read more.
Absorption systems are a sustainable solution as solar driven air conditioning devices in places with warm climatic conditions, however, the reliability of these systems must be improved. The absorbing component has a significant effect on the cycle performance, as this process is complex and needs efficient heat exchangers. This paper presents an experimental study of a bubble mode absorption in a plate heat exchanger (PHE)-type absorber with NH3-LiNO3 using a vapor distributor in order to increase the mass transfer at solar cooling operating conditions. The vapor distributor had a diameter of 0.005 m with five perforations distributed uniformly along the tube. Experiments were carried out using a corrugated plate heat exchanger model NB51, with three channels, where the ammonia vapor was injected in a bubble mode into the solution in the central channel. The range of solution concentrations and mass flow rates of the dilute solution were from 35 to 50% weight and 11.69 to 35.46 × 10−3 kg·s−1, respectively. The mass flow rate of ammonia vapor was from 0.79 to 4.92 × 10−3 kg·s−1 and the mass flow rate of cooling water was fixed at 0.31 kg·s−1. The results achieved for the absorbed flux was 0.015 to 0.024 kg m−2·s−1 and the values obtained for the mass transfer coefficient were in the order of 0.036 to 0.059 m·s−1. The solution heat transfer coefficient values were obtained from 0.9 to 1.8 kW·m−2·K−1 under transition conditions and from 0.96 to 3.16 kW·m−2·K−1 at turbulent conditions. Nusselt number correlations were obtained based on experimental data during the absorption process with the NH3-LiNO3 working pair. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle The Feasibility of Improving the Accuracy of In Situ Measurements in the Air-Surface Temperature Ratio Method
Energies 2018, 11(7), 1885; https://doi.org/10.3390/en11071885
Received: 15 June 2018 / Revised: 10 July 2018 / Accepted: 16 July 2018 / Published: 19 July 2018
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Abstract
This paper reports on a feasibility study conducted to improve the in situ measurement accuracy of the air-surface temperature ratio (ASTR) method. The measured relative error rate was analyzed using the ISO 6946 [7.69 W/(m2·K)] and Korea Energy Saving Design Standard
[...] Read more.
This paper reports on a feasibility study conducted to improve the in situ measurement accuracy of the air-surface temperature ratio (ASTR) method. The measured relative error rate was analyzed using the ISO 6946 [7.69 W/(m2·K)] and Korea Energy Saving Design Standard [9.09 W/(m2·K)] indoor total surface heat transfer coefficients. The relative error rate was analyzed according to fluctuations in outdoor temperature data. The relative error rate obtained using the ISO 6946 standard was analyzed about 6.3% and that obtained using the Korea Energy Saving Design Standard was about 9.5%. The relative error rate measured for outdoor temperature fluctuations of less than 1 K was about 4.62% and that for temperatures greater than 1 K was about 14.31%. The study results confirmed the cause of the error in the measurement of the ASTR. It was also found that the accuracy of the latter can be improved when the ISO 6946 indoor total surface heat transfer coefficient is applied and when outdoor temperature fluctuations less than 1 K are sampled and analyzed. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Multi-Objective Optimisation of the Energy Performance of Lightweight Constructions Combining Evolutionary Algorithms and Life Cycle Cost
Energies 2018, 11(7), 1863; https://doi.org/10.3390/en11071863
Received: 19 June 2018 / Revised: 11 July 2018 / Accepted: 16 July 2018 / Published: 17 July 2018
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Abstract
This paper discusses the thermal and energy performance of a detached lightweight building. The building was monitored with hygrothermal sensors to collect data for building energy model calibration. The calibration was performed using a dynamic simulation through EnergyPlus® (EP) (Version 8.5, United
[...] Read more.
This paper discusses the thermal and energy performance of a detached lightweight building. The building was monitored with hygrothermal sensors to collect data for building energy model calibration. The calibration was performed using a dynamic simulation through EnergyPlus® (EP) (Version 8.5, United States Department of Energy (DOE), Washington, DC, USA) with a hybrid evolutionary algorithm to minimise the root mean square error of the differences between the predicted and real recorded data. The results attained reveal a good agreement between predicted and real data with a goodness of fit below the limits imposed by the guidelines. Then, the evolutionary algorithm was used to meet the compliance criteria defined by the Passive House standard for different regions in Portugal’s mainland using different approaches in the overheating evaluation. The multi-objective optimisation was developed to study the interaction between annual heating demand and overheating rate objectives to assess their trade-offs, tracing the Pareto front solution for different climate regions throughout the whole of Portugal. However, the overheating issue is present, and numerous best solutions from multi-objective optimisation were determined, hindering the selection of a single best option. Hence, the life cycle cost of the Pareto solutions was determined, using the life cycle cost as the final criterion to single out the optimal solution or a combination of parameters. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Energy Prices, Real Estate Sales and Industrial Output in China
Energies 2018, 11(7), 1847; https://doi.org/10.3390/en11071847
Received: 9 May 2018 / Revised: 28 June 2018 / Accepted: 5 July 2018 / Published: 14 July 2018
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Abstract
A majority of energy is consumed to control the indoor environment for human activities and industrial production. The demand for energies for these two uses are reflected in demand for different types of real estate and the volume of industrial outputs. The purpose
[...] Read more.
A majority of energy is consumed to control the indoor environment for human activities and industrial production. The demand for energies for these two uses are reflected in demand for different types of real estate and the volume of industrial outputs. The purpose of this study is to examine the long-run equilibrium and short-run dynamics between real energy prices and demand for different types of real estate and industrial output in China. Energy prices are measured in the real price of fuels and power. Demand for different types of real estate is measured in their sales volume in the first hand market, that is, floor areas of new real estate sold by developers. Industrial output is measured by the net output (value added) of the industrial sector. All data series were tested for stationarity (i.e., the existence of a unit root) before testing for a co-integration relationship. We found no long-term equilibrium relationship between energy prices and the demand for real estate and industrial output as predicted by theory, probably due to increased supply of energy efficient buildings. There is also no short-run relationship between energy prices and demand for housing due to the increase in vacancy rate resulting from speculative demand for housing. However, demand for commercial properties appeared to lead energy prices. Finally, there is strong evidence suggesting that an increase in energy prices will significantly reduce industrial output but not vice versa. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Artificial Neural Network–Based Control of a Variable Refrigerant Flow System in the Cooling Season
Energies 2018, 11(7), 1643; https://doi.org/10.3390/en11071643
Received: 23 May 2018 / Revised: 18 June 2018 / Accepted: 20 June 2018 / Published: 24 June 2018
PDF Full-text (6379 KB) | HTML Full-text | XML Full-text
Abstract
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next
[...] Read more.
This study aimed to develop a control algorithm that can operate a variable refrigerant flow (VRF) cooling system with optimal set-points for the system variables. An artificial neural network (ANN) model, which was designed to predict the cooling energy consumption for upcoming next control cycle, was embedded into the control algorithm. By comparing the predicted energy for the different set-point combinations of the control variables, the control algorithm can determine the most energy-effective set-points to optimally operate the cooling system. Two major processes were conducted in the development process. The first process was to develop the predictive control algorithm which embedded the ANN model. The second process involved performance tests of the control algorithm in terms of prediction accuracy and energy efficiency in computer simulation programs. The results revealed that the prediction accuracy between simulated and predicted outcomes proved to have a low coefficient of variation root mean square error (CVRMSE) value (10.30%). In addition, the predictive control algorithm markedly saved the cooling energy consumption by as much as 28.44%, compared to a conventional control strategy. These findings suggest that the ANN model and the control algorithm showed potential for the prediction accuracy and energy-effectiveness of VRF cooling systems. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Boundary Conditions Accuracy Effect on the Numerical Simulations of the Thermal Performance of Building Elements
Energies 2018, 11(6), 1520; https://doi.org/10.3390/en11061520
Received: 15 April 2018 / Revised: 1 June 2018 / Accepted: 6 June 2018 / Published: 11 June 2018
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Abstract
Numerical simulation is widely used in the field of computational building physics for the definition of the thermal performance of building elements. An integral component of numerical simulation using finite elements is the boundary conditions, which, in the case of simulating the thermal
[...] Read more.
Numerical simulation is widely used in the field of computational building physics for the definition of the thermal performance of building elements. An integral component of numerical simulation using finite elements is the boundary conditions, which, in the case of simulating the thermal performance of a building element, are usually expressed in terms of the external surface temperature as a function of time. The purpose of this study is to examine the effect of the accuracy of the boundary conditions on the thermal performance simulation of building elements. The assumption that the temperature versus time is a sinusoidal function, applied in standard methods, is comparatively assessed with the actual function for diverse climatic conditions using finite elements simulation. The findings of the analysis indicate that the sinusoidal function fails to accurately simulate real boundary conditions. The originality of this study lies within the adoption of a signal reconstruction algorithm, which follows a novel approach by reconstructing the actual temperature versus time signal for the simulation of the actual boundary conditions. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Convex Programming and Bootstrap Sensitivity for Optimized Electricity Bill in Healthcare Buildings under a Time-Of-Use Pricing Scheme
Energies 2018, 11(6), 1454; https://doi.org/10.3390/en11061454
Received: 8 May 2018 / Revised: 26 May 2018 / Accepted: 31 May 2018 / Published: 5 June 2018
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Abstract
Efficient energy management is strongly dependent on determining the adequate power contracts among the ones offered by different electricity suppliers. This topic takes special relevance in healthcare buildings, where noticeable amounts of energy are required to generate an adequate health environment for patients
[...] Read more.
Efficient energy management is strongly dependent on determining the adequate power contracts among the ones offered by different electricity suppliers. This topic takes special relevance in healthcare buildings, where noticeable amounts of energy are required to generate an adequate health environment for patients and staff. In this paper, a convex optimization method is scrutinized to give a straightforward analysis of the optimal power levels to be contracted while minimizing the electricity bill cost in a time-of-use pricing scheme. In addition, a sensitivity analysis is carried out on the constraints in the optimization problems, which are analyzed in terms of both their empirical distribution and their bootstrap-estimated statistical distributions to create a simple-to-use tool for this purpose, the so-called mosaic-distribution. The evaluation of the proposed method was carried out with five-year consumption data on two different kinds of healthcare buildings, a large one given by Hospital Universitario de Fuenlabrada, and a primary care center, Centro de Especialidades el Arroyo, both located at Fuenlabrada (Madrid, Spain). The analysis of the resulting optimization shows that the annual savings achieved vary moderately, ranging from −0.22 % to +27.39%, depending on the analyzed year profile and the healthcare building type. The analysis introducing mosaic-distribution to represent the sensitivity score also provides operative information to evaluate the convenience of implementing energy saving measures. All this information is useful for managers to determine the appropriate power levels for next year contract renewal and to consider whether to implement demand response mechanisms in healthcare buildings. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Investigation of Airflow Patterns in a New Design of Wind Tower with a Wetted Surface
Energies 2018, 11(5), 1100; https://doi.org/10.3390/en11051100
Received: 26 March 2018 / Revised: 19 April 2018 / Accepted: 27 April 2018 / Published: 30 April 2018
PDF Full-text (7721 KB) | HTML Full-text | XML Full-text
Abstract
Passive cooling systems, such as wind towers, can help to reduce energy consumption in buildings and at the same time reduce greenhouse gas (GHG) emissions. Wind towers can naturally ventilate buildings and also can create enhanced thermal comfort for occupants during the warm
[...] Read more.
Passive cooling systems, such as wind towers, can help to reduce energy consumption in buildings and at the same time reduce greenhouse gas (GHG) emissions. Wind towers can naturally ventilate buildings and also can create enhanced thermal comfort for occupants during the warm months. This study proposes a modern wind tower design with a moistened pad. The new design includes a fixed column, a rotating and movable head, an air opening with a screen, and two windows at the end of the column. The wind tower can be installed on roof-tops to take advantage of ambient airflow. The wind tower’s head can be controlled manually or automatically to capture optimum wind velocity based on desired thermal condition. To maximize its performance, a small pump was considered to circulate and spray water on an evaporative cooling pad. A computational fluid dynamics (CFD) simulation of airflow around and inside the proposed wind tower is conducted to analyze the ventilation performance of this new design of wind tower. Thereby, the velocity, total pressure, and pressure coefficient distributions around and within the wind tower for different wind velocities are examined. The simulation results illustrate that the new wind tower design with a moistened pad can be a reasonable solution to improve naturally the thermal comfort of buildings in hot and dry climates. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Load Disaggregation via Pattern Recognition: A Feasibility Study of a Novel Method in Residential Building
Energies 2018, 11(4), 1008; https://doi.org/10.3390/en11041008
Received: 20 February 2018 / Revised: 5 April 2018 / Accepted: 18 April 2018 / Published: 20 April 2018
PDF Full-text (7458 KB) | HTML Full-text | XML Full-text
Abstract
In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined in residential buildings. Experiments were designed
[...] Read more.
In response to the need to improve energy-saving processes in older buildings, especially residential ones, this paper describes the potential of a novel method of disaggregating loads in light of the load patterns of household appliances determined in residential buildings. Experiments were designed to be applicable to general residential buildings and four types of commonly used appliances were selected to verify the method. The method assumes that loads are disaggregated and measured by a single primary meter. Following the metering of household appliances and an analysis of the usage patterns of each type, values of electric current were entered into a Hidden Markov Model (HMM) to formulate predictions. Thereafter, the HMM repeatedly performed to output the predicted data close to the measured data, while errors between predicted and the measured data were evaluated to determine whether they met tolerance. When the method was examined for 4 days, matching rates in accordance with the load disaggregation outcomes of the household appliances (i.e., laptop, refrigerator, TV, and microwave) were 0.994, 0.992, 0.982, and 0.988, respectively. The proposed method can provide insights into how and where within such buildings energy is consumed. As a result, effective and systematic energy saving measures can be derived even in buildings in which monitoring sensors and measurement equipment are not installed. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle A Domestic Microgrid with Optimized Home Energy Management System
Energies 2018, 11(4), 1002; https://doi.org/10.3390/en11041002
Received: 28 February 2018 / Revised: 10 April 2018 / Accepted: 13 April 2018 / Published: 20 April 2018
Cited by 1 | PDF Full-text (1555 KB) | HTML Full-text | XML Full-text
Abstract
Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for
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Microgrid is a community-based power generation and distribution system that interconnects smart homes with renewable energy sources (RESs). Microgrid efficiently and economically generates power for electricity consumers and operates in both islanded and grid-connected modes. In this study, we proposed optimization schemes for reducing electricity cost and minimizing peak to average ratio (PAR) with maximum user comfort (UC) in a smart home. We considered a grid-connected microgrid for electricity generation which consists of wind turbine and photovoltaic (PV) panel. First, the problem was mathematically formulated through multiple knapsack problem (MKP) then solved by existing heuristic techniques: grey wolf optimization (GWO), binary particle swarm optimization (BPSO), genetic algorithm (GA) and wind-driven optimization (WDO). Furthermore, we also proposed three hybrid schemes for electric cost and PAR reduction: (1) hybrid of GA and WDO named WDGA; (2) hybrid of WDO and GWO named WDGWO; and (3) WBPSO, which is the hybrid of BPSO and WDO. In addition, a battery bank system (BBS) was also integrated to make our proposed schemes more cost-efficient and reliable, and to ensure stable grid operation. Finally, simulations were performed to verify our proposed schemes. Results show that our proposed scheme efficiently minimizes the electricity cost and PAR. Moreover, our proposed techniques, WDGA, WDGWO and WBPSO, outperform the existing heuristic techniques. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Reliability Field Test of the Air–Surface Temperature Ratio Method for In Situ Measurement of U-Values
Energies 2018, 11(4), 803; https://doi.org/10.3390/en11040803
Received: 31 January 2018 / Revised: 19 March 2018 / Accepted: 23 March 2018 / Published: 30 March 2018
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Abstract
This study proposes the air–surface temperature ratio (ASTR) method as an in situ measurement method to rapidly and accurately measure wall U-values in existing houses. Herein, the wall U-values were measured in situ applying the heat flow meter (HFM) method of ISO 9869-1
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This study proposes the air–surface temperature ratio (ASTR) method as an in situ measurement method to rapidly and accurately measure wall U-values in existing houses. Herein, the wall U-values were measured in situ applying the heat flow meter (HFM) method of ISO 9869-1 and the ASTR method. The results obtained using the HFM and ASTR methods were compared, and the relative error rate and accuracy of the measurements were analyzed. The aging rates of the wall U-values were compared and analyzed by comparing them with the wall U-values before and after the installation of retrofit insulation. Subsequently, the ASTR method was used to analyze the U-value measurement error rates according to the number of measurement days (one day to seven days). In addition, this method calculated the appropriate measurement period required to satisfy the measurement conditions. As a result, the mean relative measurement errors rates of the HFM and ASTR methods were ±3.21%. The short-term (one day) and long-term (seven days or longer) measurement results indicated the average error rates as approximately ±2.63%. These results were included in the tolerance range. Therefore, it was determined that the ASTR method can rapidly and accurately measure wall U-values. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units
Energies 2018, 11(3), 611; https://doi.org/10.3390/en11030611
Received: 19 January 2018 / Revised: 13 February 2018 / Accepted: 5 March 2018 / Published: 9 March 2018
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Abstract
With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and
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With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Thermal Assessment of a Novel Combine Evaporative Cooling Wind Catcher
Energies 2018, 11(2), 442; https://doi.org/10.3390/en11020442
Received: 5 January 2018 / Revised: 6 February 2018 / Accepted: 13 February 2018 / Published: 15 February 2018
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Abstract
Wind catchers are one of the oldest cooling systems that are employed to provide sufficient natural ventilation in buildings. In this study, a laboratory scale wind catcher was equipped with a combined evaporative system. The designed assembly was comprised of a one-sided opening
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Wind catchers are one of the oldest cooling systems that are employed to provide sufficient natural ventilation in buildings. In this study, a laboratory scale wind catcher was equipped with a combined evaporative system. The designed assembly was comprised of a one-sided opening with an adjustable wetted pad unit and a wetted blades section. Theoretical analysis of the wind catcher was carried out and a set of experiments were organized to validate the results of the obtained models. The effect of wind speed, wind catcher height, and mode of the opening unit (open or closed) was investigated on temperature drop and velocity of the moving air through the wind catcher as well as provided sensible cooling load. The results showed that under windy conditions, inside air velocity was slightly higher when the pad was open. Vice versa, when the wind speed was zero, the closed pad resulted in an enhancement in air velocity inside the wind catcher. At wind catcher heights of 2.5 and 3.5 m and wind speeds of lower than 3 m/s, cooling loads have been approximately doubled by applying the closed-pad mode. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes
Energies 2018, 11(2), 384; https://doi.org/10.3390/en11020384
Received: 17 December 2017 / Revised: 1 February 2018 / Accepted: 2 February 2018 / Published: 7 February 2018
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Abstract
With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In
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With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessFeature PaperArticle Development of Easily Accessible Electricity Consumption Model Using Open Data and GA-SVR
Energies 2018, 11(2), 373; https://doi.org/10.3390/en11020373
Received: 29 December 2017 / Revised: 26 January 2018 / Accepted: 30 January 2018 / Published: 5 February 2018
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Abstract
In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized
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In many countries, DR (Demand Response) has been developed for which customers are motivated to save electricity by themselves during peak time to prevent grand-scale blackouts. One of the common methods in DR, is CPP (Critical Peak Pricing). Predicting energy consumption is recognized as one of the tool for dealing with CPP. There are a variety of studies in developing the model of energy consumption, which is based on energy simulation, data-driven model or metamodelling. However, it is difficult for general users to use these models due to requirement of various sensing data and expertise. And it also takes long time to simulate the models. These limitations can be an obstacle for achieving CPP’s purpose that encourages general users to manage their energy usage by themselves. As an alternative, this research suggests to use open data and GA (Genetic Algorithm)–SVR (Support Vector Regression). The model is applied to a hospital in Korea and 34,636 data sets (1 year) are collected while 31,756 (11 months) sets are used for training and 2880 sets (1 month) are used for validation. As a result, the performance of proposed model is 14.17% in CV (RMSE), which satisfies the Korea Energy Agency’s and ASHRAE (American Society of Heating, Refrigerating and Air-Conditioning Engineers) error allowance range of ±30%, and ±20% respectively. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Open AccessArticle Determining the U-Value of Façades Using the Thermometric Method: Potentials and Limitations
Energies 2018, 11(2), 360; https://doi.org/10.3390/en11020360
Received: 26 December 2017 / Revised: 30 January 2018 / Accepted: 1 February 2018 / Published: 3 February 2018
Cited by 3 | PDF Full-text (12226 KB) | HTML Full-text | XML Full-text
Abstract
The thermal transmittance of building envelopes determines to a large extent the energy demand of buildings. Thus, there is a keen interest in having methods which can precisely evaluate thermal transmittance. From a scientific point of view, this study analyses the viability of
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The thermal transmittance of building envelopes determines to a large extent the energy demand of buildings. Thus, there is a keen interest in having methods which can precisely evaluate thermal transmittance. From a scientific point of view, this study analyses the viability of the application of the thermometric method (THM), one of the most used methods in Spain. For this purpose, the test method has been improved by determining the adequate test conditions, the selection and installation of equipment, data acquisition and post-processing, and the estimation of uncertainty. We analyse eight case studies in a Mediterranean climate (Csa) to determine the potentials and limitations of the method. The results show that the values obtained through THM are valid under winter environmental conditions with relative uncertainties between 6% and 13%, while difficulties to perform the test in optimal conditions, and therefore to obtain valid results in warmer seasons, are detected. In this regard, the case studies which obtained a greater number of observations by performing the filtrate conditions were able to obtain representative results. Furthermore, there are significant differences depending on the kind of equipment and probes used during the experimental campaign. Finally, in warm climate regions a data filtrate can be considered for observations of a temperature difference higher than 5 °C, obtaining valid results for the case studies, although the rise in the thermal gradient can guarantee a greater stability of data. Full article
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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