Advanced Strategies for NetZero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle
Abstract
:1. Introduction
1.1. Research Background
1.2. Outline
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 Part A—Passive strategies: To help realize nZEB through the reduction of the building energy demand (e.g., heating and cooling load, etc.) by introducing architectural design techniques in the early design stage [6]. Also, it can be divided into the following two categories: Part A1: passive sustainable design; and Part A2: energysaving techniques (EST);
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 Part B—Active strategies: After reducing the building energy demand through passive strategies, the residual load can be saved via the active strategies, such as RE [6]. There are two categories of active strategies for implementing nZEB: Part B1: RE; and Part B2: a backup system for RE.
2. A Holistic Review of the Implementation Strategies of nZEB
2.1. Part A: Passive Strategies
2.1.1. Part A1: Passive Sustainable Design
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 Building geometry: Building energy demand is greatly influenced by a building’s composition and shape. Thus, almost all the previous studies related to building geometry evaluated building energy performance by focusing on the surrounding environment (i.e., site slope) and plan form (refer to Table 1) [8,9,10,11,12,13,14,15]. For example, De Castro and Gadi (2017) compared the annual energy savings according to the site slope within the range of 0~50° to find out the ideal design by considering the topography. As a result, it is shown that the 30° site slope and boxtype design is the optimal design with the highest energysaving potential via the ‘EnergyPlus’ software program [8]. Choi et al. (2012) investigated the energy consumption patterns according to four highriseapartment plan layouts (e.g., plate and tower type) and two living types (i.e., generaluse, mixeduse) through questionnaires and a field study for evaluating the building energy performance (i.e., electricity consumption, gas consumption, and CO_{2} emissions). This study showed that the electricity consumption of platetype buildings was lower than that of towertype buildings, but their gas consumption was higher. In addition, from the perspective of a building’s living type, mixeduse buildings generated more CO_{2} emissions than the general residential buildings [10]. Asadi et al. (2014) and Mottahedi et al. (2015) evaluated the energy consumption using the multilinear regression analysis and Monte Carlo simulation by considering a total of 7 building shapes (i.e., L, U, T, H, triangle, rectangle, rectangle mincorner) and 17 design variables (e.g., orientation, insulation, occupant schedule, etc.). As a result of the analysis of the annual energy consumption according to the seven building shapes, it was determined that the H shape of the building in the Texas climate zone had the highest energy consumption among all the shapes studied [11,15].
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 Natural lighting: Through an indepth analysis of the sun’s altitude, the amount of daylight, etc., many researchers have conducted various studies on how to determine the optimal orientation of a building and on designing the atrium from the perspective of introducing the natural light system for nZEB implementation (refer to Table 2) [16,17,18,19,20,21,22,23,24,25,26,27,28]. First, there are various studies related to determination of the optimal orientation of a building for reducing the building energy demand [16,17,18,19,20]. Abanda and Byer (2016) assessed the impact of the building orientation on building energy consumption using building information modeling according to the following threestep process: (i) building design through the Revit software program; (ii) conversion to a numerical value based on Green Building Studio, one of the leading energy simulation programs; and (iii) analysis of the effect of building orientations on the annual energy usage of a building. From the analysis results, the optimal building orientation in terms of the annual electricity and gas consumption was derived as south (i.e., building orientation of +180° from north), and the difference in energy cost savings throughout a 30 year period between the best and worst (i.e., the orientation of +45° from north) orientation was determined as £878 [17]. Hemsath (2016) aimed to analyze the impact of the building orientation on the building energy consumption and annual costs for four different geographical locations in U.S. (i.e., Lincoln, New York, Miami, and Phoenix) at the community and individual house levels. Compared to an individual house’s optimal orientation, the mean of planning that considers the optimal building orientation at a community level was analyzed to more effectively reduce the annual energy cost [19]. Second, various studies have been carried out for efficient atrium design because the atrium shape is a very important factor in natural lighting [21,22,23,24,25,26,27,28]. Nasrollashi et al. (2015) assessed the impact of the atriumtototal building area ratio in terms of the energy efficiency and indoor environmental conditions, using the DesignBuilder software program. As a result of this study, setting the atriumtototal building area ratio as 1/4 was determined to be the most effective in terms of energy performance, daylighting, and thermal comfort (i.e., predicted mean vote) [25]. Mohsenin and Hu (2015) evaluated the daylight of an office building according to the atrium type (i.e., central, attached, and semienclosed), atrium proportions (i.e., Well Index), and roof aperture designs (i.e., monitor roof and horizontal skylight) through the DIVA software program as the climatebased daylighting modeling tool [26].
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 Natural ventilation: To implement nZEB, an architectural design for inducing the reduction of the building energy demand through the effective influx of the outdoor air (i.e., natural ventilation) should be considered in the initial design stage. Generally, natural ventilation can be categorized based on two mechanisms: (i) buoyancydriven ventilation by the vertical and horizontal temperature difference; and (ii) winddriven ventilation by the pressure difference between the front and the back of the building (refer to Table 3) [29,30,31,32,33,34,35,36,37,38]. First, there are various previous studies related to buoyancydriven ventilation [29,30,31,32,33,34]. Li and Liu (2014) focused on the thermal performance of phasechangematerial (PCM)based solar chimney within laboratory conditions with three different heat fluxes (500, 600, and 700 W/m^{2}). Through this study, it was confirmed that PCMbased solar chimney can achieve the timeshifting of solar energy, which can induce more effective natural ventilation compared to the general solar chimney, based on the large thermal energy storage capacity of PCM [31]. Acred and Gary (2014) proposed a design strategy of stack effect ventilation for a multistory atrium building based on a simplified mathematical model. The dimensionless charts that can determine the combination of design variables were developed as a guideline for realizing natural a ventilated building [30]. Second, there are various previous studies related to winddriven ventilation [35,36,37,38]. Nejat et al. (2016) conducted a comparative analysis of the windcatcherintegrated wing wall (i.e., new design) and the conventional wind catcher via the Computational Fluid Dynamics (CFD) software program and wind tunnel testing. As a result, the ventilation performance of the windcatcher with a 30° wing wall angle was superior to the other designs (45° and 60°). Also, the ventilation performance of the new design was improved by 50% compared to the conventional wind catcher [37]. Mei et al. (2017) analyzed the ventilation performance according to the building density level in an urban residential neighborhood using the CFD software program. In addition, this study presented a strategy for establishing the optimal neighborhood building layout design in terms of ventilation performance (i.e., pollutant level) [38].
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 Discussion: In this study, research related to passive sustainable design was analyzed, focusing on building geometry, natural lighting, and natural ventilation. First, from the viewpoint of building geometry, previous studies have focused on energy savings according to the building shape and building density. Second, from the viewpoint of natural lighting, most previous studies centered on reducing the lighting, cooling, and heating load depending on the shape and size of the atrium and the building’s orientation. Finally, from the view point of natural ventilation, previous studies analyzed the energysaving potential and ventilation performance focused on buoyancydriven ventilation and winddriven ventilation. In other words, if a building is designed with factors of building geometry, natural lighting, and natural ventilation being taken into consideration at the beginning of the process, the following effects can be obtained: 20% energy efficiency improvement, 25% heating, and 10–30% cooling load reduction [12,21,33]. However, it is still insufficient to consider only passive sustainable design in terms of implementing nZEB. Therefore, future studies from the perspective of nZEB need to be applied not only with passive sustainable design, but also with energysaving techniques (EST) and active strategies.
2.1.2. Part A2: EnergySaving Techniques (EST)
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 Building envelope design: As the building envelope is directly against the external environment, it plays an important role in energy consumption (e.g., heating and cooling demand) [39]. Accordingly, a variety of studies have been carried out in relation to the reduction of the building energy demand through the envelope design. In this study, the abovementioned past studies can be classified into heat insulation, opening design, and shading device (refer to Table 4) [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55]. First, in terms of the heat insulation, the studies that have been carried out to reduce buidling energy demand are as follows [40,41,42,43,44,45]. Pomponi et al. (2015) evaluated CO_{2} emissions and energy consumptions in terms of building life cycle by comparing various façade strategies (i.e., doubleskin facade, traditional uptostandard, and single skin). As a result of the analysis, it was confirmed that applying the doubleskin façade of building has the best carbonsaving potential [44]. Tam et al. (2016) assessed the technical performance and costeffectiveness of the green roof as a heat insulation in Hong Kong through a questionnaire survey, interviews, and field studies. Also, the results showed that the room temperature can be lowered by 3.4 °C when the green roof is applied [45]. Second, in terms of the window design, most of the previous studies mainly focused on the derivation of an optimal design solution considering that the window is more vulnerable to heat gain and loss than the wall is [46,47,48,49,50,51]. Goia (2016) examined the optimal windowtowall ratio (WWR) for four cities (i.e., Oslo, Frankfurt, Rome, and Athens) located in the midlatitude region of Euroupe through the EnergyPlus software program. These cities all showed an optimal energy performance within the range of 30–45% of the WWR [49]. Wen et al. (2017) aimed to develop a guideline that would enable the designer to determine the suitability of the WWR in the early design stage. As a result, the distribution of the optimal WWR in Japan was mapped out by considering the window properties (e.g., Uvalue, visible transmittance, etc.) and meteorological factors (i.e., mean external temperature and mean global solar radiation) [50]. Finally, in terms of the shading device, several studies have been conducted to foind a way of lowering the building energy demand [52,53,54,55]. Kim et al. (2012) investigated the various type of external shading devices (e.g., overhang, blind, etc.) in terms of energy savings for heating and cooling, via the IES_VE software program. Through this study, it was concluded that the external shading device had a better technical performance than the internal shading device [52].
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 Heat storage system: Various studies associated with the heat storage system have been conducted because the heat capacity of a building is a very important factor from the point of view of nZEB. This study examined the previous studies that analyzed the energy reduction according to the building thermal performance, focusing on the thermal mass and trombe wall (refer to Table 5) [56,57,58,59,60,61,62,63,64,65,66]. First, there are many previous studies that focused on reducing the heating and cooling demand of a building through the heat storage function of the thermal mass [56,57,58,59,60,61]. Ma and Wang (2012) conducted a numerical analysis of the dynamic heat transfer performance of the interior planer thermal mass according to the thermal mass thickness (i.e., 0.025~0.70 m) and type (i.e., wood, concrete, and steel). It was found that the heat storage ability of the thermal mass relies on the thermal mass thickness for reaching a superlative value [58]. Chernounsov and Chan (2016) analyzed the thermal performance of the buildingenvelopeintegrated PCM with a high specific heat capacity using the EnergyPlus software program for an office building in Hong Kong. Through this study, the relationship between the indoor thermal environment and the PCM’s thickness, placement, and orientation was analyzed [61] Second, the past studies related to the trombe wall, which functions as a heat storage system by applying a solar heating collector made of double glazing on the wall, are as follows [62,63,64,65,66]. Bojic et al. (2014) conducted a comparative analysis of the environmental performance (i.e., primary energy for heating during winter and annual energy consumption) according to the application of the trombe wall. As a result of the analysis, it was shown that 20% annual energysaving is possible when the trombe wall is applied [64] Bajc et al. (2015) focused on the impact analysis of the building energy demand of a passive house with the trombe wall via CFD simulation considering the Belgrade weather. The results showed that the trombe wall increased the cooling demand in summer, but it is very suitable for the Belgrade climate because of its efficient heating energysaving in winter [66].
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 Lighting design: From the perspective of nZEB implementation, this study analyzed the previous studies focused on the lighting emitting diode (LED), light shelves, and lighting control system as methods for reducing the lighting load (refer to Table 6) [67,68,69,70,71,72,73,74]. Principi and Fioretti (2014) conducted a comparative analysis of the compact fluorescent and LED in terms of their environmental performance, based on the experimental test results. Moreover, it is possible to save up to 41~50% global warming potential and cumulative energy demand by using LED rather than the compact fluorescent [68]. Meresi (2016) investigated the efficiency of daylight for the allocation of light shelves and movable semitransparent external blind considering various design conditions, via the Radiance software program. As a result, the combination of a light shelf and semitransparent movable external blinds can increase the daylight exploitation and can construct a uniform illuminance distribution in a room by increasing the light level at the back of the space and reducing the daylight near the window [72]. Byun et al. (2014) developed an intelligent LED control system considering the energy consumption and user satisfaction, based on multisensors and wireless communication technology. The proposed LED control system showed 21.9% energy savings by automatically adjusting the illuminance considering the energy efficiency [67].
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 Discussion: In this study, existing studies related to EST are analyzed focusing on the building envelope design, heat storage system, and lighting design. In particular, due to the characteristics of the building envelope directly facing the external environment, lots of studies related to building envelope design and energy demand have been carried out, taking into consideration heat insulation, window design, and shading device. Also, for the heat storage system, various studies are under way to increase the heat storage performance of a building by applying advanced materials such as PCM. Finally, leading on from studies regarding the introduction of LEDs, studies in terms of lighting design focusing on a reduction of the lighting load through the introduction of a control system considering daylight and shading devices are being carried out. As previously mentioned, various studies are underway to reduce building energy demands, but it is not enough to focus on only EST for practical nZEB implementation. In other words, to realize nZEB, active strategies must be considered together with EST as passive strategies.
2.2. Part B: Active Strategies
2.2.1. Part B1: Renewable Energy (RE)
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 PV system: The previous studies related to the PV system mainly performed technicaleconomicpolicy analysis in terms of two perspectives (i.e., rooftop PV system and buildingintegrated PV system (BIPV)) (refer to Table 7) [75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95]. First, there have been various studies on the rooftop PV system [75,76,77,78,79,80,81,82]. Ordóñez et al. (2010) investigated the energy capacity of the PV system in Andalusia, Spain considering the residential building characterization (e.g., detached house, townhouse, etc.), useful rooftop area, and PV panel installation design (i.e., distance between solar panels) using the Autodesk AutoCAD software program. According to this study, the amount of electricity generated from PV systems on a residential building’s rooftop (i.e., 265.52 km^{2} of the total roof surface area) is 9.73 GW/year, which is 78.89% of the total energy requirements [75]. Elibol et al. (2017) was carried out outdoor testing of the technical performance of PV panels for one year on the roof of Düzce university scientific and technology researches application and research center in Düzce Province, Turkey according to the PV panel’s type (i.e., monocrystalline, polycrystalline, and amorphous silicon (aSi)). As a result, the efficiencies of the PV panel were 4.79, 11.36, and 13.26% for the aSi, polycrystalline, and monocrystalline PV panel, respectively. In addition, the external temperature correlated positively with the aSi and polycrystalline PV panel, and negatively correlated with the monocrystalline PV panel [81]. Hong et al. (2017) developed a method for predicting the amount of electricity generated from the rooftop PV system through hill shade analysis, by assessing the potential of three perspectives (i.e., physical, geographic, and technical potential). As a result of applying the developed methodology to the Gangnam district in Seoul, South Korea, the physical, geographic (i.e., the available rooftop area), and technical potentials of Gangnam district were 9,178,982 MWh, 4,964,118 m^{2}, and 1,130,371 MWh [82]. Second, there have been various studies on the BIPV [83,84,85,86,87,88,89,90,91,92,93,94,95]. Olivieri et al. (2014) evaluated the technical performance of the windowintegrated semitransparent PV system and general glazing via a package of specific software program (i.e., DesignBuilder, EnergyPlus, PVsyst, and COMFEM). It was confirmed that the windowintegrated semitransparent PV system showed 18–59% energy savings according to the façade opening compared with the reference glass [90]. Oh et al. (2017) developed a ninenodebased finite element model for estimating the technoeconomic performance of buildingintegrated PV blind system (BIPB) through MicrosoftExcel based VBA. In addition, the economic analysis of BIPB was conducted focusing on the residential progressive electricity tariffs [95].
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 Solar thermal system: There have been various studies that utilize solar heat by absorbing, storing, and converting it for the heating and cooling of a building based on infinite solar energy (refer to Table 8) [96,97,98,99,100,101,102,103,104,105,106,107]. Anderson et al. (2010) analyzed the effect of the color (ranging from white to black) of the solar collector both theoretically and experimentally on the thermal performance of the buildingintegrated solar thermal system [96]. Mammoli et al. (2010) analyzed the technoeconomicenvironmental performance of a solarthermalassisted HVAC system by considering the season, operation time, temperature (e.g., tank temperature, solar array inlet/out let temperature, etc.), and solar heat data (e.g., solar flux, solar collector’s efficiency, etc.) through experiment evaluation [97]. Bornatico et al. (2012) developed a model that could represent the optimal capacity of the solar thermal system through the particle swarm optimization algorithm and genetic algorithm by considering the meteorological data, collector area, tank volume, and size of the auxiliary power unit [99]. Chialastri and Isaacson (2017) conducted tests for a prototype of a buildingintegrated PV/thermal air collector which can generate thermal and electrical energy based on experiments and twodimensional models in COMSOL Multiphysics. As a result, the maximum temperature of the prototype was 31 °C, and the average thermal and electrical efficiencies were 31% and 7%, respectively [107].
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 Geothermal system: Many studies have been conducted on the geothermal system that can reduce the heating and cooling demand of a building based on a constant annual underground temperature of 15 °C (refer to Table 9) [108,109,110,111,112,113,114,115,116,117,118,119]. Kim et al. (2012) conducted an evaluation of the performance of the geothermal system installed in Pusan national university in South Korea based on the measured data (e.g., outdoor and indoor temperature, inlet and outlet temperature of circulating water, etc.). Towards this end, this study installed thermocouples under the ground for analyzing the characteristics of the geothermal heat exchanger’s thermal diffusion, and estimated the technical performance of the geothermal system according to the heating and cooling period [34]. Sivasakthivel et al. (2012) assessed the potential reduction in CO_{2} emissions and the potential for electricitysaving by introducing the geothermal system during winter in the northern region of India, considering the regional factors and the coefficient of performance of the geothermal system. This study indicated that applying the geothermal system can reduce the CO_{2} emissions and electricity consumption by 0.539 and 708 GW, respectively [111]. Kim et al. (2015) conducted a comprehensive analysis of the economic and environmental performance of the geothermal system according to the entering water temperature from life cycle perspective, using the GLHEPro software program [115].
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 Wind turbine system: As the wind speed is a very important factor for the wind turbine system, most of the previous studies on it analyzed its technical performance by applying it to the rooftop or to a highrise building (refer to Table 10) [120,121,122,123,124,125,126,127,128]. Li et al. (2013) assessed the feasibility of implementing the wind turbine system in a tall building through wind tunnel tests. As a result, the building orientation, the bellmounted shapes of the four tunnels with contracted inner sections, and the surrounding buildings were found to be important factors influencing the wind speed amplification and wind loads [124]. Lu and Sun (2014) analyzed the technical potential of wind power in urban highrise buildings considering the wind data and building properties. Toward this end, a numerical analysis of the simulation results was conducted using the CFD and ANSYS FLUENT software program [125]. Cao et al. (2017) evaluated the wind power resource around the 1000meter scale of megatall buildings in China based on the mesoscale meteorological model Weather Research and WRF v3.4 software program. According to the results of this study, the technical performance of the wind turbine system was seen to be the best when at distances of 300 and 200 m from the ground, and when the building orientation is north and south, in terms of the wind power density and the amount of electricity generated [128].
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 Discussion: The four RE (i.e., PV system, solar thermal system, geothermal system, and wind turbine system)related researches in this study showed the following trends. First, studies related to the PV system were mainly focused on the technoeconomic performance analysis according to the PV panel type (e.g., aSi panel, polycrystalline panel, monocrystalline panel, and semitransparent PV system) and the development of the prediction model of the amount of electricity generated according to the design variables of the PV system. Second, related studies of the solar thermal system focused primarily on the thermal performance of a building according to the characteristics of the solar collector (e.g., color, capacity, temperature, etc.). Third, for the geothermal system, the majority of studies carried out on energy savings and economic effects depend on design conditions (e.g., a given geothermal system’s coefficient of performance, location, borehole length, etc.). Lastly, for the wind turbine system applied to building, studies were conducted mainly on highrise building in order to analyze the amount of electricity generated and optimal design conditions by considering climate (e.g., wind date), building layout, and so on. Various efforts such as the application of high efficiency PV panels and analysis on optimal design conditions of the RE system have been carried out to improve energy selfsufficiency rate of a building through RErelated studies, but it would be very difficult to implement nZEB with only RE.
2.2.2. Part B2: BackUp Systems for RE
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 Fuel cell system: The fuel cell system is an electricity power generator that utilizes electricity produced through the chemical reaction of hydrogen and oxygen. Furthermore, the fuel cell system can be more effectively used when applied with RE. This is because the electricity produced from RE can be used in the electrolysis of water in the fuel cell system [129]. The previous studies related to the fuel cell system are as follows (refer to Table 11) [130,131,132,133,134,135]. Hong et al. (2014) aimed to develop a framework for optimally applying the fuelcellbased combined heat and power system to a multifamily housing complex. Also, in order to verify the feasibility of developed framework, this study evaluated the fuelcellbased heatandpowercombined system for ‘O’ apartment in Seoul, South Korea from the perspectives of primary energy savings, life cycle cost, and life cycle CO_{2} [130]. Ansong et al. (2017) conducted a technoeconomic performance analysis of the hybrid electric power system (i.e., PV system, fuel cell system, and diesel generator) for an offgrid mine company using the HOMER software program to select the optimal energy system. As a result, it was shown that the optimal electric power system could produce 152.99 GWh electricity over a year when composed of 50 MW of PV system, 15 MW of fuel cell system, and 20 MW of diesel generator [135].
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 ESS: RE is heavily affected by electricity generation based on the outdoor environment conditions (e.g., solar radiation, wind strength, etc.). To solve this problem, various studies are being conducted on the ESS as a backup system that can store the electricity generated from the RE. In this study, the previous studies related to ESS are classified into those on the thermal ESS and those on the electrical ESS depending on the type of stored energy (refer to Table 12) [136,137,138,139,140,141,142]. First, the previous studies conducted in terms of thermal ESS are as follows [136,137,138,139]. Alimohammadisagvand et al. (2016) aimed to find a costoptimal solution for thermal ESS integrated with the geothermal system for a residential building in a cold climate based on the concept of demand response (DR) (i.e., a momentary DR control based on the realtime hourly electricity price, a backwardslooking DR control based on the previous hourly electricity price, and a predictive DR control based on future hourly electricity price). The analysis result showed that applying the predictive DR control algorithm is most effective in terms of the annual savings in the total delivered energy and cost [136]. Al Zahrani and Dincer (2016) conducted a performance evaluation of the aquifer thermal ESS considering the charging temperature, storing time, temperature during storing, discharging temperature, etc. To this end, this study analyzed focusing on the energy and exergy during the heating and cooling period using the Engineering Equation Solver software program [137]. Second, the previous studies that were conducted in terms of the electrical ESS are as follows [140,141,142]. Vieira et al. (2017) considered the ESS connected with the PV system of a residential building as a system for matching the energy production and consumption in Coimbra, Portugal. The results indicated that the ESS connected with the PV system can reduce the energy sent to and consumed from grid by 76 and 78.3%, respectively, as well as the energy bill by 87.2% [142].
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 Discussion: In this study, studies regarding the backup system were analyzed focusing on the fuel cell system and ESS in terms of effective operation of the RE. First, from the perspective of the fuel cell system, the technoeconomic performance analysis was conducted mainly based on simulation tools. Second, from the perspective of the ESS, various studies are underway so as to analyze the technical and economic effects based on DR strategies combined with RE. In terms of the backup system, especially for ESSrelated research, it is considered to be more effective in terms of energy savings because DR strategies are applied so as to analyze the technical performance by considering energy demand and supply. However, there will be limitations in obtaining optimal technoeconomic effects because existing studies related to this are based on historical data (e.g., monthly or yearly data) rather than realtime data.
3. Future Directions and Challenges for Realizing nZEB
3.1. Integration and Optimization of the Passive and Active Strategies in the Early Phase of a Building’s Life Cycle
3.1.1. Integration of the Passive and Active Strategies
3.1.2. Optimization of the Passive and Active Strategies for Determining the Optimal Design Solution
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 Step 1—Development of an integrated analysis model for estimating the building energy demand and supply: Those who want to implement nZEB (e.g., CMr and designer) should be able to easily and quickly analyze the building energy demand and supply for the application of the passive and active strategies at the early phase of a building’s life cycle. An examination of the previous studies in this regard revealed that Koo et al. (2014) and Park et al. (2016) developed an estimation model for the building energy demand (i.e., heating and cooling demand) and supply (i.e., the amount of electricity generated from the distributed solar generation system) based on fournodebased Lagrangian shape function that is easy to use during the early design phase (refer to Figure 3, Figure A1 and Figure A2). These studies, however, also have limitations in evaluating the energy performance in terms of nZEB because the energy demand and supply were analyzed separately according to the building envelope design [39,91]. Therefore, developing an integratedanalysis model of energy demand and supply for applying the active and passive strategies’ technology in the early stage of a building’s life cycle remains a toppriority challenge for implementing nZEB as shown in Figure 4.
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 Step 2—Development of an integrated multiobjective optimization (iMOO) model for determining the optimal design solution for the realization of nZEB: In the early stage of a building’s life cycle, the CMr and designer should take into account not only the building’s energy demand and supply, but also the building’s economic performance (e.g., NPV, SIR, and PP) depending on the design conditions (e.g., WWR, window type, windowtoPV panel ratio, etc.). As these considerations, however, have a tradeoff relationship, it is very difficult for the CMr and designer to decide the optimal design plan by incorporating all the considerations at the initial stage of a building’s life cycle [105,153]. Therefore, it is necessary to develop an iMOO model to help in the CMr and designer’s decisionmaking and can implement a reasonable nZEB. In the previous studies, several methodologies have been developed to address the tradeoff relationship between the building energy performance (e.g., energy demand and supply) and the building’s economic performance (e.g., NPV, SIR, and PP). Koo et al. (2015) proposed an iMOO model based on the concept of the Pareto front, which can provide the optimal design solution to the users by solving the tradeoff problems between the construction time and cost, according to the following six processes: (i) problem statement; (ii) definition of the optimization objectives; (iii) establishment of the data structure; (iv) standardization of the optimization objective function; (v) definition of the fitness function; and (vi) implementation of the genetic algorithm (refer to Figure 5) [154]. In addition, based on this paper, Koo et al. (2016) and Kim et al. (2016) developed an iMOO model for determining the optimal solution in implementing RE, considering the technoeconomic performance (refer to Figure A3) [105,153]. The iMOO model suggested in the aforementioned studies has the advantages of solving the tradeoff problems between various optimization objectives (i.e., energy demand or supply, NPV, SIR, and PP) and deriving the optimal solution in a userfriendly design. When deriving the optimal design solution, however, the aforementioned studies had limitations in that they did not consider the building energy demand and supply simultaneously. In other words, from the standpoint of the effective implementation of nZEB, the iMOO model must be achieve in the direction of minimizing the building energy demand (e.g., heating and cooling demand) and optimizing the energy supply (e.g., the amount of electricity generated from PV system) and economic performance (e.g., NPV, SIR, and PP). Therefore, from the viewpoint of the integration of the passive and active strategies to implement nZEB, the development of the iMOO model considering the both energy demand and supply remains a challenge for many researchers (refer to Figure 6).
3.2. RealTime Monitoring of the Energy Performance during the Usage Phase of a Building’s Life Cycle
3.2.1. Developing an Integrated RealTime Monitoring System for Building Energy Performance
3.2.2. Analyzing the EnergySaving Potential through the EndUser Behaviors
4. Conclusions
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 Passive strategies: The passive strategies refer to reducing the building energy demands at the early stage of a building’s life cycle through an architectural design technique. In this study, passive strategies were classified as passive sustainable design and EST. Most studies related to the passive strategies (i.e., passive sustainable design and EST) were analyzed for building energy performance according to design variables via energy simulation tools. Analysis of these studies showed that applying passive strategies to buildings is effective in terms of energy savings, but it is not sufficient in terms of implementing nZEB.
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 Active strategies: The active strategies mainly represent ways to reduce building energy consumption through energy production. This study conducted an extensive literature review on these active strategies focusing on the RE and backup system for RE. The studies regarding active strategies (i.e., RE and backup system for RE) mostly analyzed building energy performance through experiments with energy simulation tools. The analysis of the previous studies showed that RE is still not enough to realize nZEB, and in case of the backup system, especially ESS, the technical and economic effects may be lower because they complement the RE based on historical data.
 (1)
 Integration and optimization of the passive and active strategies in the early phase of a building’s life cycle: This study proposed integration and optimization of the passive and active strategies as the advanced strategies for implementing nZEB in order to overcome the limitations of previous studies by the following two perspectives: (i) there are very few studies evaluating buildings technical and economic performance by applying both passive and active strategies; (ii) the technical effects (e.g., energy savings) that occur through sequential application of passive and active strategies are far superior.
 (2)
 Realtime monitoring of the energy performance during the usage phase of a building’s life cycle: This study presented the following two aspects of realtime monitoring of the energy performance as advanced strategies so as to realize nZEB by considering the limitations of existing studies and diversification of buildings: (i) developing an integrated realtime monitoring system for buildings’ energy performance; and (ii) analyzing the energysaving potential through the enduser behaviors.
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
aSi  Amorphous silicon 
BIPB  Buildingintegrated photovoltaic blind system 
BIPV  Buildingintegrated photovoltaic system 
CFD  Computational fluid dynamics 
CMr  Construction manager 
DR  Demand response 
ESS  Energy storage system 
EST  Energysaving techniques 
GHG  Greenhouse gas 
iMOO model  Integrated multiobjective optimization model 
LED  Lighting emitting diode 
NPV  Net present value 
nZEB  Netzero energy building 
PCM  Phase change material 
PP  Payback period 
PV  Photovoltaic 
RE  Renewable energy 
SIR  Savingtoinvestment ratio 
WWR  Windowtowall ratio 
Appendix A
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Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

TuhusDubrow et al. (2010) [9]  Total seven building shape (e.g., rectangle and cross shape)  Optimal building shape, life cycle cost/utility cost  Genetic algorithm  USA 

Choi et al. (2012) [10]  Building type (i.e., mixeduse and general apartment), shape (i.e., tower and plate type)  Electricity/gas consumption  Survey and research  Korea 

Parasonis et al. (2012) [12]  Building density, relative geometry efficiency (i.e., ratio of surface area to building volume)  Annual energy savings  Mathematical model  Lithuania 

Parasonis et al. (2012) [13]  Building density, relative geometry efficiency (i.e., ratio of surface area to building volume)  Annual energy savings  Mathematical model  Lithuania 

Asadi et al. (2014) [11]  Total seven building shapes (e.g., triangle and rectangle shapes)  Annual energy consumption  Multilinear regression analysis  USA 

Hemsath and Bandhosseini (2015) [14]  Ratio of plan’s length to width, stacking level  Annual energy consumption  Global sensitivity analysis  USA 

Mottahedi et al. (2015) [15]  Total seven building shapes (i.e., triangle, rectangle, mincomer, L, U, T, and H Shapes)  Annual energy consumption  Multilinear regression analysis  USA 

De Castro and Gadi (2017) [8]  Total five slopeadaptive building designs (e.g., singlelevel and spiltlevel), site slope level within the range of 0~50°  Monthly average load, annual energy saving  EnergyPlus  Portugal 

Classification  Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

Orientation  Fallahtafti and Mahdavinejad (2015) [16]  Orientation, building geometry, windowtowall ratio (WWR)  Heating gain/loss  Mathematical model  Iran 

Abanda and Byer (2016) [17]  U.K electricity price, gas price, orientation (interval: 45°)  Annual CO_{2} emissions  Revit, BIM, Green Building Studio  UK 
 
de Vasconcelos et al. (2016) [18]  Orientation, discount rate  Global energy cost, energy consumption  Life cycle cost analysis  Portugal 
 
Hemsath (2016) [19]  Orientation, climate factors  Annual energy costs, optimal orientation  BEopt, EnergyPlus  USA 
 
ValladaresRendon et al. (2017) [20]  Climate factors  Worldwide guide of azimuth angle  Case study, Ecotect  Hong Kong (China) 
 
Atrium  Assadi et al. (2011) [21]  Glass height and diameter  Thermal efficiency  Mathematical model  Iran 

Aldawoud (2013) [22]  Atrium dimensions, atrium geometry ratio  Annual energy consumption  DOE2.1  Saudi Arabia 
 
Chow et al. (2013) [23]    Energy savings of each floors  Field study  Hong Kong (China) 
 
Taleghani et al. (2014) [24]  Wind speed  Energy demand  EnergyPlus  The Netherlands 
 
Nasrollashi et al. (2015) [25]  Atrium ratio, internal heat load  Annual energy consumption, comfort indices  Mathematical model  Iran 
 
Mohsenin and Hu (2015) [26]  Well index (i.e., atrium proportion), atrium types (e.g., semienclosed), aperture types (e.g., horizontal skylight)  Useful daylight illuminance  DIVA  USA 
 
Danielski et al. (2016) [27]  Building design (e.g., atrium), envelope parameter (e.g., Uvalue)  Energy demand  Questionnaire, VIPEnergy software  Sweden 

.  Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

Buoyancydriven ventilation  Hussain and Patrick (2012) [29]  Solar intensity, floor property (e.g., depth, width, and height)  Volume flow rate, thermal comfort  CFD simulation   

Acred and Gary (2014) [30]  Heat transfer parameter  Air flow rate  Mathematical method  UK 
 
Li and Liu (2014) [31]  Solar intensity (i.e., 500–700 W/m^{2}), phase change material (PCM) thermosphysical properties  Air flow rate  Mathematical method  UK 
 
Jing et al. (2015) [32]  Solar intensity (i.e., heat flux), gap to height ratio (i.e., 0.2~0.6)  Air flow rate  Experiment  China 
 
He et al. (2017) [33]  Architectural design (e.g., orientation, building envelope), climate  Energy savings, thermal comfort  Mathematical method  China 
 
Tong et al. (2017) [34]  Weather (e.g., temperature, wind), season  Natural ventilation potential  Atmospheric boundary layer, meteorology model  USA 
 
Winddriven ventilation  Afshin et al. (2016) [35]  Wind angle, inlet wind speed  Air flow rate  Mock up experiment  Iran 

Nejat et al. (2016) [36]  Wind speed, windcatcher angle (i.e., 30, 60, 90°)  Air flow rate, CO_{2} concentration  Experiment, CFD simulation  Malaysia 
 
Nejat et al. (2016) [37]  Wind speed, wing wall angle (i.e., 30, 45, 60°)  Air change hour, air flow rate  Mock up experiment  Malaysia 
 
Mei et al. (2017) [38]  Building densities (i.e., medium (0.25) and compact (0.44) urban development)  Wind flow  CFD simulation  China 

Classification  Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

Heat insulation  Daouas (2011) [40]  Orientation, insulation property  Annual energy load, optimum insulation thickness  Mathematical model  Tunisia 

Ottele et al. (2011) [41]  Green wall type (e.g., bare, direct, indirect)  Energy savings  Life cycle environmental analysis  The Netherlands 
 
Sanjuan et al. (2011) [42]  Openjoint ventilated façade, conventional façade  Heat flux, solar radiation  CFD simulation  Spain 
 
Hong et al. (2013) [43]  Double skin façade type (i.e., box, corridor, multistory, shaft)  Savingtoinvestment ratio, breakeven point  Life cycle cost analysis  Korea 
 
Pomponi et al. (2015) [44]  Façade type (i.e., double skin/mono), glass type (i.e., clear/coated)  Heating load, payback period  Life cycle energy and environmental analysis  UK 
 
Tam et al. (2016) [45]    Measured temperature, maintenance cost  Survey and research  Hong Kong (China) 
 
Opening design  Su and Zhang (2010) [46]  WWR, orientation, glass type (e.g., single, hollow)  Environmental impact  Life cycle environmental analysis  China 

MA et al. (2015) [47]  WWR (i.e., 10~100% with interval 10%), Uvalue  Recommended WWR  Mathematical model  USA 
 
Seo et al. (2015) [48]  WWR, orientation, glazing type, shading type  Heating/cooling demand  DesignBuilder v3.0, MicrosoftExcelbased VBA  Korea 
 
Goia (2016) [49]  WWR, orientation, building geometry, façade material  Total energy consumption, Daylight autonomy  EnergyPlus  E.U. 
 
Wen et al. (2017) [50]  WWR (i.e., 10~70%, interval: 10%), orientation  Total CO_{2} emission, recommended WWR  EnergyPlus  Japan 
 
Zenginis and Kontoleon (2017) [51]  WWR, building aspect ratio (i.e., length and width dimensions)  Heat gain and loss  Mathematical model  Greek 
 
Shading device  Kim et al. (2012) [52]  Shading device type (i.e., overhang, blind, lightshelf), device slat angle  Energy saving, annual heating load  EnergyPlus  Korea 

Cheng et al. (2013) [53]  Width/height of opening, ratio of length of shade device to window vertical length  Shading ratio  Mathematical model  Taiwan 
 
Lin et al. (2016) [54]  Sunshade style and size (i.e., horizontal, vertical, grid), envelope material  Optimal sunshade  TRNSYS  Taiwan 
 
Ye et al. (2016) [55]  Shading type (i.e., internal, external, and nonshading)  Indoor temperature, solar radiation intensity, cooling load  EnergyPlus  China 

Classification  Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

Thermal mass  Diaconu (2011) [56]  PCM melting point, ventilation frequency, occupancy pattern  Indoor temperature, energy savings  TRNSYS  Romania 

Weinlaeder (2011) [57]  Conventional/PCM integrated interior blind  Indoor/outdoor temperature  Experiment  Germany 
 
Ma and Wang (2012) [58]  Thermal mass thickness  Indoor temperature, maximum heat storage  Mathematical method  USA 
 
Silva et al. (2015) [59]  Daily average solar radiation, wind direction, and intensity  Indoor/outdoor temperature  Experiment   
 
Turner et al. (2015) [60]  Precooling period  Energy consumption, peak load reduction  REGCAP  USA 
 
Chernousov and Chan (2016) [61]  Thermal mass property, internal load fraction  Indoor temperature, AC power demand  EnergyPlus  Hong Kong (China) 
 
Trombe wall  Stazi et al. (2012) [62]  Wall material (e.g., concrete), glazing type, wall thickness, frame material  CO_{2} emissions  EnergyPlus, Life cycle environmental analysis  Italy 

Abbassi et al. (2014) [63]  Trombe wall area (i.e., 0~19 m^{2}, interval: 1 m^{2})  Heating energy savings  TRNSYS  Tunisia 
 
Bojic et al. (2014) [64]  Real residential house description (e.g., wall material)  Primary energy use, energy saving  EnergyPlus  France 
 
BrigaSa et al. (2014) [65]  Massive wall thickness (i.e., 15~40 cm, interval: 5 cm)  Heating load  Mathematical method  Portugal 
 
Bajc et al. (2015) [66]  Boundary condition (e.g., material density)  Indoor temperature, heat flux  CFD simulation  Servia 

Authors  Design Variables  Analysis Target  Simulation Tool or Method  Country  Main Finding 

Byun et al. (2014) [67]  Occupancy time, occupant movement detection  Lighting intensity  Experiment  Korea 

Principi and Fioretti (2014) [68]  Life cycle inventory (e.g., land use, cumulative energy demand, etc.)  Environment impact  Life cycle environmental analysis  Italy 

Xue et al. (2014) [69]  Distance from window  Illuminance, uniformity ratio (i.e., minimal illuminance/average illuminance)  Radiance, TracePro7.0  Hong Kong (China) 

Berardi and Anaraki (2015) [70]  Location of illuminance measurement, WWR  Useful daylight illuminance  Radiance  Canada 

Nagy et al. (2015) [71]  Occupancy time, control mode (i.e., baseline, comfort, and savings)  Daily energy consumption  Experiment  Switzerland 

Meresi (2016) [72]  Location of illuminance  Daylight level/factor  Radiance  Greek 

Caicedo et al. (2017) [73]  Daylight factor, occupancy time, ceiling sensor position  Dimming level  Experiment  The Netherlands 

Lee et al. (2017) [74]  Vent ratio (4, 6, 8, and 10 mm of diameter), angle of lightshelf  Indoor illumination, energy consumption  Experiment  Korea 

Classification  Research  Analysis Target  Design Variables  Simulation Tool or Method  Country  Main Findings  

Technical ^{a}  Economic ^{b}  Policy ^{c}  
Rooftop PV system (Horizontal)  Ordóñez et al. (2010) [75]  ○      Building properties, shading effect, PV installation type, solar irradiation loss from shading  AutoCAD, Google Earth  Spain 

Hong et al. (2014) [76]  ○      Geographical/meteorological information  RETScreen, ArcMap 10.1, Mathematical method  Korea 
 
Koo et al. (2014) [77]  ○  ○  ○  Building information, PV panel’s properties, region  Mathematical method, RETScreen  Korea 
 
Jeong et al. (2015) [78]  ○  ○    Military building’s information, PV panel/inverter type  Mathematical method, RETScreen, PBECAS  Korea 
 
Park et al. (2016) [79]  ○    ○  Building/PV system/region information  RETScreen, Mathematical method  Korea 
 
Ban et al. (2017) [80]  ○  ○    Information of the PV panels/inverters/region, roof’s properties  Mathematical method RETScreen  Korea 
 
Elibol et al. (2017) [81]  ○      PV panel type (e.g., monocrystalline and polycrystalline), amount of radiation, panel temperature  Experiment, statistical analysis  Turkey 
 
Hong et al. (2017) [82]  ○      Building data, region (e.g., altitude), shaded area, fixedtilt system, tracker system  Mathematical method, Vworld  Korea 
 
Buildingintegrated PV system (Vertical)  Lu and Yang (2010) [83]  ○  ○    Weather data, solar radiation, orientation, inclined angle of PV  Mathematical method, LCA  Hong Kong (China) 

Radhi (2010) [84]  ○      Regional factors, design, and performance of BIPV system  MeteoNorm software, Energy10 software  UAE 
 
Hong et al. (2011) [85]  ○  ○    Energy consumption, energy consumption/per occupant  Mathematical method  Korea 
 
Buildingintegrated PV system (Vertical)  Hammond et al. (2012) [86]  ○  ○  ○  PV installation/electricity cost, financial support policy  Ecoindicator 99, Mathematical method  UK 

Hwang et al. (2012) [87]  ○      Weather data, building information, details of PV panels  Building management system of Samsung, CAD, Equest  Korea 
 
Chae et al. (2014) [88]  ○  ○    Ufactor, solar heat gain coefficient, visible transmittance, source energy unit price  Mathematical method, EnergyPlus  Korea 
 
Lee et al. (2014) [89]  ○      PV panel’s efficiency, building load  Mathematical method  Korea 
 
Olivieri et al. (2014) [90]  ○  ○    Glazing properties, climate data, building information  DeignBuilder, EnergyPlus, PVSyst, COMFEN  Spain 
 
Park et al. (2016) [91]  ○  ○  ○  Region, visible transmittance, exterior window area, windowtoPV panel ratio, PV panel’s efficiency  Finite element method, Microsoft excel based VBA  Korea 
 
Peng et al. (2016) [92]  ○      Semitransparent PV’s properties, infrared thermal emissivity and thermal conductivity  EnergyPlus, mathematical model, sensitivity analysis  USA 
 
Hong et al. (2017) [93]  ○  ○  ○  Architectural design variables, window design variables, buildingintegrated PV blind (BIPB) design variables  Autodesk Ecotect Analysis, mathematical method  Korea 
 
Koo et al. (2017) [94]  ○  ○  Type of PV panel/tracking system, building data  RETScreen, ArcMap 10.3, mathematical method  Korea 
 
Oh et al. (2017) [95]  ○  ○  ○  Electricity rate in South Korea, region, orientation, visible transmittance, PV panel efficiency, wall and window area, WWR, windowtoPV panel ratio  Finite element method, Microsoft excel based VBA  Korea 

Research  Analysis Target  Design Variables  Simulation Tool or Method  Country  Main Findings  

Technical ^{a}  Economic ^{b}  
Anderson et al. (2010) [96]  ○    Color of building integrated solar collector  Mathematical method  New Zealand 

Mammoli et al. (2010) [97]  ○  ○  Solar flux, flow rate and input/output, solar collector’s efficiency, absorption chiller’s performance, heating/cooling system operation time  TRNSYS  Mexico 

Ampatzi and Knight (2012) [98]  ○    Building information, weather data, infiltration/ventilation rates, internal gains, hot water consumption  TRNSYS, TRNbuild, ECOTECH  Wales (UK) 

Bornatico et al. (2012) [99]  ○    Meteorological data, collector area, tank volume, auxiliary power unit size  MATLAB, Polysun, PSO algorithm, mathematical method  Switzerland 

Fong and Alwan (2013) [100]  ○    Weather data, cooling profile  TRNSYS, IES, mathematical method  UK 

Motte et al. (2013) [101]  ○    Solar irradiance, ambient temperature, wind speed, input/output water temperature  Mathematical method, MATLAB  France 

Lamnatou et al. (2014) [102]  ○    Characteristic of the building integrated solar thermal system, life cycle inventory  Mathematical method  France 

Li et al. (2014) [103]  ○    Operation control parameters, storage characteristics, matching degree between solar collector area and system integral capacity  TRNSYS, mathematical method  China 

Maurer et al. (2015) [104]  ○    Uvalue , ambient temperature, room temperature, zerogvalue  Mathematical method   

Kim et al. (2016) [105]  ○    Region, azimuth/slope/type of collector, storage type, rooftop area, minimum heat generation limit, maximum budget limit  Mathematical method, Microsoft Excel based VBA  Korea 

Araya et al. (2017) [106]  ○  ○  Solar flatplate collector arrangement, water tank, auxiliary system, energy demand  Genetic algorithms, MATLAB, LCC  Chile 

Chialastri and Isaacson (2017) [107]  ○  ○  Glazing type (e.g., uncoated, Lowe double), material (e.g., aluminum), air speed, temperature  Experiment, twodimensional model in COMSOL Multiphysics  USA 

Research  Analysis Target  Design Variables  Simulation Tool or Method  Country  Main Findings  

Technical ^{a}  Economic ^{b}  
Wood et al. (2010) [108]  ○    Date, average air temperature, season  EED, GLHEPRO, Mathematical method  UK 

Desideri et al. (2011) [109]  ○    Design of underground heat exchangers, soil type, number/total length/distance between the boreholes, energy demand, heating/cooling plant installation cost  TRNSYS 16  Italy 

Kim et al. (2012) [110]  ○  ○  Heat pump type (e.g., inverter type), outdoor/indoor temperature, relative humidity  Experiment  Korea 

Sivasakthivel et al. (2012) [111]  ○  ○  Region (e.g., several/moderate cold), COP of GSHP  Mathematical method  India 

Kang et al. (2013) [112]  ○    Season, climate weather data  Mathematical method, MATLAB/Simulink  France, Korea 

Self et al. (2013) [113]  ○  ○  Variable of geothermal heat pump    Europe 

Morrone et al. (2014) [114]  ○  ○  Ground characteristic, building information, performance of heat pump/cooling machine, loading conditions for heating and cooling  Mathematical method, DOCET, PILESIM2  Italy 

Kim et al. (2015) [115]  ○  ○  Region, geothermal heat exchanger properties, heating/cooling loads, entering water temperature  Mathematical method, LCI & LCC Analysis, GLHEPro  Korea 

Kharseh et al. (2015) [116]  ○  ○  Building/GSHP specification, driving energy of A/C system  Earth energy designer, HAP  Qatar 

Lee et al. (2015) [117]  ○  ○  Specifications of the vertical/horizontal GCHE, outdoor temperature, electric power, energy consumption  TRNSYS  Korea 

Hong et al. (2016) [118]  ○    Borehole length/spacing/diameter, grout thermal conductivity, Upipe diameter/spacing  Mathematical method, GLHEPro  Korea 

Jeong et al. (2017) [119]  ○    Geographic, annual average temperature, annual heating days, ground heat exchanger’s characteristics, threshold fluid temperature  FEM, G.POT, Kriging method  Korea 

Research  Input Data  Output Data  Simulation Tool or Method  Country  Main Findings 

Sharpe and Proven (2010) [120]  Blade design, pitch control/angle, pitch control of rotor rpm  Development of true building integrated wind turbine  Stream tube model, Mathematical method, μwind  UK 

Walker (2011) [121]  Wind speed, theoretical power curves, turbine performance  Measurement of power production  Mathematical method, CFD, BREVe  UK 

Ayhan and Sağlam (2012) [122]  Geometry scenarios and building layout, assembly forms to the building of wind turbines  Feasibility of wind power utilization  CFD, mathematical method  Turkey 

Balduzzi et al. (2012) [123]  Wind turbine’s characteristics, city data (e.g., building height)  Flow velocity modulus and direction, attended capacity factor, new turbine model  CFD, Reynoldsaveraged Navier–Stokes (RANS)  Europe 

Li et al. (2013) [124]  Wind tunnel tests, wind climate data analysis  Wind loads on tall buildings, wind speed up factors in the tunnels for windpower generation  Mathematical method, real model test  China 

Lu and Sun (2014) [125]  Wind/building data  Wind power utilization into or on urban highrise buildings  CFD, ANSYS/FLUENT, UDF  Hong Kong (China) 

Park et al. (2014) [126]  Region, building information, characteristic of small wind power generation system  Noise and vibration, amount of power generated by the small wind power system  CFD, mockup test  Korea 

Yang et al. (2016) [127]  Climate data, wind velocity and direction, turbulence intensity, complex urban topography of the studied site  Maximum power density with optimum height  ANSYS/Fluent, SIMPLEC, CFD, RANS  Taiwan 

Cao et al. (2017) [128]  Building information, wind data, heights of the first five layers in the meteorological data  Annual average power output/electric power yield of building integrated wind turbine system  Mathematical method, CFD, WRF Model  China 

Research  Analysis Target  Design Variables  Simulation Tool or Method  Country  Main Findings  

Technical ^{a}  Economic ^{b}  
Hong et al. (2014) [130]  ○  ○  Fuel cell combined heat and power system’s operating scheme and size, energy demand/supply  Mathematical method, Crystal Ball  Korea 

Adam et al. (2015) [131]  ○    Building information, region, occupancy schedule  IES VE, Mathematical method  UK 

Kim et al. (2014) [132]  ○  ○  Building type, operating scheme, operating size, energy demand/supply  Mathematical method, MicrosoftExcelbased VBA  Korea 

Sossan et al. (2014) [133]  ○  ○  Building indoor temperature, fuel cell and tank’s properties, optimization horizon length, maximum fuel cell off–on cycles  CTSM, LabView, mathematical method   

Elmer et al. (2016) [134]  ○  ○  Trigeneration system energetic performance  Mathematical method  UK 

Ansong et al. (2017) [135]  ○  ○  Site location, electrical load, solar resource, diesel fuel’s information  HOMER software  Ghana 

Classification  Research  Analysis Target  Input Data  Simulation Tool or Method  Country  Main Findings  

Technical ^{a}  Economic ^{b}  
Thermal energy storage system  Alimohammadisagvand et al. (2016) [136]  ○  ○  Indoor/storage tank temperature set point, hourly electricity price, weather, building information, HVAC system  IDA ICE, MonteCarlo simulation, NIDAQ  Finland 

Al Zahrani and Dincer (2016) [137]  ○    Dis/charging temperature, mass flow rate, storing time, temperature drop/rise during storing, ambient temperature  Mathematical method, EES  Canada 
 
Jin et al. (2017) [138]  ○  ○  External walls and windows’ heat transfer, internal heat gains, heat contribution, solar radiation, cooling power generated  Mathematical method  USA 
 
Jradi et al. (2017) [139]  ○  ○  Monthly global solar irradiation in Odense and PV system yield, heating energy satisfied by PVdriven heat pump, electrical energy demand/supply  Mathematical method, MATLAB  Denmark 
 
Electrical energy storage system  Connolly et al. (2012) [140]  ○  ○  Electricity demands, energy storage system’s capacities/efficiencies, regulation strategies, fuel cost, distribution data (e.g., heat, electric)  Energy PLAN, mathematical method  Ireland 

Ma et al. (2015) [141]  ○    The presence or absence of supercapacitor  MATLAB/Simulink, mathematical method   
 
Vieira et al. (2017) [142]  ○    Solar radiation, energy consumption, PV panel, and battery information  MATLAB/Simulink, mathematical method, PVSyst  Portugal 

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Oh, J.; Hong, T.; Kim, H.; An, J.; Jeong, K.; Koo, C. Advanced Strategies for NetZero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle. Sustainability 2017, 9, 2272. https://doi.org/10.3390/su9122272
Oh J, Hong T, Kim H, An J, Jeong K, Koo C. Advanced Strategies for NetZero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle. Sustainability. 2017; 9(12):2272. https://doi.org/10.3390/su9122272
Chicago/Turabian StyleOh, Jeongyoon, Taehoon Hong, Hakpyeong Kim, Jongbaek An, Kwangbok Jeong, and Choongwan Koo. 2017. "Advanced Strategies for NetZero Energy Building: Focused on the Early Phase and Usage Phase of a Building’s Life Cycle" Sustainability 9, no. 12: 2272. https://doi.org/10.3390/su9122272