Greenhouse Building Energy Simulation(BES)models were developed to estimate the energy load using TRNSYS(ver.16,University of Wisconsin,USA),a commercial BES program.Validation was conducted based on data recorded dur...Greenhouse Building Energy Simulation(BES)models were developed to estimate the energy load using TRNSYS(ver.16,University of Wisconsin,USA),a commercial BES program.Validation was conducted based on data recorded during field experiments.The BES greenhouse modeling is reliable,as validation showed 5.2%and 5.5%compared with two field experiments,respectively.As the next step,the heating characteristics of the greenhouses were analyzed to predict the maximum and annual total heating loads based on the greenhouse types and target locations in the Republic of Korea using the validated greenhouse model.The BES-computed results indicated that the annual heating load was greatly affected by the local climate conditions of the target region.The annual heating load of greenhouses located in Chuncheon,the northernmost region,was 44.6%higher than greenhouses in Jeju,the southernmost area among the studied regions.The regression models for prediction of maximum heating load of Venlo type greenhouse and widespan type greenhouse were developed based on the BES computed results to easily predict maximum heating load at field and they explained nearly 95%and 80%of the variance in the data set used,respectively,with the predictor variables.Then a BES model of geothermal energy system was additionally designed and incorporated into the BES greenhouse model.The feasibility of the geothermal energy system for greenhouse was estimated through economic analysis.展开更多
A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between infor...A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.展开更多
Despite known effects of urban heat island(UHI)on building energy consumption such as increased cooling energy demand,typical building energy simulation(BES)practices lack a standardized approach to incorporate UHI in...Despite known effects of urban heat island(UHI)on building energy consumption such as increased cooling energy demand,typical building energy simulation(BES)practices lack a standardized approach to incorporate UHI into building energy predictions.The seasonal and diurnal variation of UHI makes the task of incorporating UHI into BES an especially challenging task,often limited by the availability of detailed hourly temperature data at building location.This paper addresses the temporal variation of UHI by deriving four normalized UHI indicators that can successfully capture the seasonal and diurnal variation of UHI.The accuracy of these indicators was established across four climate types including hot and humid(Miami,FL),hot and dry(Los Angeles,CA),cold and dry(Denver,CO),and cold and humid(Chicago,IL),and three building types including office,hospital,and apartments.These four indicators are mean summer daytime UHI,mean summer nighttime UHI,mean winter daytime UHI,and mean winter nighttime UHI,which can accurately predict cooling,heating,and annual energy consumption with mean relative error of less than 1%.Not only do these indicators simplify the application of UHI to BES but also,they provide a new paradigm for UHI data collection,storage,and usage,specifically for the purpose of BES.展开更多
基金This work was carried out with the support of the“Cooperative Research Program for Agriculture Science&Technology Development(Project No.PJ009412)”Rural Development Administration,Republic of Korea.
文摘Greenhouse Building Energy Simulation(BES)models were developed to estimate the energy load using TRNSYS(ver.16,University of Wisconsin,USA),a commercial BES program.Validation was conducted based on data recorded during field experiments.The BES greenhouse modeling is reliable,as validation showed 5.2%and 5.5%compared with two field experiments,respectively.As the next step,the heating characteristics of the greenhouses were analyzed to predict the maximum and annual total heating loads based on the greenhouse types and target locations in the Republic of Korea using the validated greenhouse model.The BES-computed results indicated that the annual heating load was greatly affected by the local climate conditions of the target region.The annual heating load of greenhouses located in Chuncheon,the northernmost region,was 44.6%higher than greenhouses in Jeju,the southernmost area among the studied regions.The regression models for prediction of maximum heating load of Venlo type greenhouse and widespan type greenhouse were developed based on the BES computed results to easily predict maximum heating load at field and they explained nearly 95%and 80%of the variance in the data set used,respectively,with the predictor variables.Then a BES model of geothermal energy system was additionally designed and incorporated into the BES greenhouse model.The feasibility of the geothermal energy system for greenhouse was estimated through economic analysis.
基金This research project is supported by the National Research Foundation,Singapore,and Ministry of National Development,Singapore under its Cities of Tomorrow R&D Programme(CoT Award COT-V4-2020-5)the National Research Foundation,Prime Minister’s Office,Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)program through a grant to the Berkeley Education Alliance for Research in Singapore(BEARS)for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics(SinBerBEST)Program.
文摘A poorly calibrated model undermines confidence in the effectiveness of building energy simulation, impeding the widespread application of advanced energy conservation measures (ECMs). Striking a balance between information-gathering efforts and achieving sufficient model credibility is crucial but often obscured by ambiguities. To address this gap, we model and calibrate a test bed with different levels of information (LOI). Beginning with an initial model based on building geometry (LOI 1), we progressively introduce additional information, including nameplate information (LOI 2), envelope conductivity (LOI 3), zone infiltration rate (LOI 4), AHU fan power (LOI 5), and HVAC data (LOI 6). The models are evaluated for accuracy, consistency, and the robustness of their predictions. Our results indicate that adding more information for calibration leads to improved data fit. However, this improvement is not uniform across all observed outputs due to identifiability issues. Furthermore, for energy-saving analysis, adding more information can significantly affect the projected energy savings by up to two times. Nevertheless, for ECM ranking, models that did not meet ASHRAE 14 accuracy thresholds can yield correct retrofit decisions. These findings underscore equifinality in modeling complex building systems. Clearly, predictive accuracy is not synonymous with model credibility. Therefore, to balance efforts in information-gathering and model reliability, it is crucial to (1) determine the minimum level of information required for calibration compatible with its intended purpose and (2) calibrate models with information closely linked to all outputs of interest, particularly when simultaneous accuracy for multiple outputs is necessary.
文摘Despite known effects of urban heat island(UHI)on building energy consumption such as increased cooling energy demand,typical building energy simulation(BES)practices lack a standardized approach to incorporate UHI into building energy predictions.The seasonal and diurnal variation of UHI makes the task of incorporating UHI into BES an especially challenging task,often limited by the availability of detailed hourly temperature data at building location.This paper addresses the temporal variation of UHI by deriving four normalized UHI indicators that can successfully capture the seasonal and diurnal variation of UHI.The accuracy of these indicators was established across four climate types including hot and humid(Miami,FL),hot and dry(Los Angeles,CA),cold and dry(Denver,CO),and cold and humid(Chicago,IL),and three building types including office,hospital,and apartments.These four indicators are mean summer daytime UHI,mean summer nighttime UHI,mean winter daytime UHI,and mean winter nighttime UHI,which can accurately predict cooling,heating,and annual energy consumption with mean relative error of less than 1%.Not only do these indicators simplify the application of UHI to BES but also,they provide a new paradigm for UHI data collection,storage,and usage,specifically for the purpose of BES.
