Search

In response to increasingly severe weather conditions, optimization of building performance and investment provides an opportunity to consider co-benefits of thermal resilience during energy efficiency retrofits. This work aims to assess thermal resilience of buildings using building performance simulation to evaluate the indoor overheating risk under nine weather scenarios, considering historical (2010s), mid-term future (2050s), and long-term future (2090s) typical meteorological years, and heat wave years. Such an analysis is based on resilience profiles that combine six integrated indicators. A case study with a district of 92 buildings in Brazil was conducted, and a combination of strategies to improve thermal resilience was identified. Results reflect the necessity of planning for resilience in the context of climate change. This is because strategies recommended under current conditions might not be ideal in the future. Therefore, an adaptable design should be prioritized. Cooling energy consumption could increase by 48 % by the 2050s, while excessive overheating issues could reach 37 % of the buildings. Simple passive strategies can significantly reduce the heat stress. A comprehensive thermal resilience analysis should ultimately be accompanied by a thorough reflection on the stakeholders’ objectives, available resources, and planning horizon, as well as the risks assumed for not being resilient.

California has a state-wide goal of carbon neutrality by 2045. Decarbonization for disadvantaged communities (DACs) poses extra challenges due to financial, informational, language, and other barriers. This paper presents the methodology, results, and analysis of energy efficiency measures (EEMs) to save energy, reduce CO2 emission, and promote clean energy access at the district scale for two DACs in Fresno, California. The methods are broadly applicable to other neighborhoods across the U.S. 22 EEMs were identified and modelled for all residential buildings in the two DACs both individually and as packages. Results show that for energy and CO2 reduction purposes, the top performing EEM package can decrease energy use and CO₂ emissions by an average of 60%. For electrification, heat pump water heaters are a viable solution, coupled with air source or mini-split heat pumps. Replacing gasoline vehicles with electric vehicles is another important measure in electrification and reducing GHG emissions.

California has a state-wide goal of carbon neutrality by 2045. Decarbonization for disadvantaged communities (DACs) poses extra challenges due to financial, informational, language, and other barriers. This paper presents the methodology, results, and analysis of energy efficiency measures (EEMs) to save energy, reduce CO2 emission, and promote clean energy access at the district scale for two DACs in Fresno, California. The methods are broadly applicable to other neighborhoods across the U.S. 22 EEMs were identified and modelled for all residential buildings in the two DACs both individually and as packages. Results show that for energy and CO2 reduction purposes, the top performing EEM package can decrease energy use and CO₂ emissions by an average of 60%. For electrification, heat pump water heaters are a viable solution, coupled with air source or mini-split heat pumps. Replacing gasoline vehicles with electric vehicles is another important measure in electrification and reducing GHG emissions.

With climate change leading to more frequent, more intense, and longer durations of extreme weather events such as heat waves and cold snaps, it is essential to maintain safe indoor environmental conditions for occupants during such events, which may coincide with, or even cause, power outages that expose residents to health risks. Analyzing the impacts of extreme weather events on the thermal resilience of buildings can help stakeholders (including occupants) understand the risk and inform them about mitigation and adaptation actions. Moreover, analyzing the technological, social and policy dimensions of thermal resilience is critical for climate-proofing buildings. This paper presents 10 questions that highlight the most important issues regarding the thermal resilience of buildings for occupants in the face of climate change. The proposed questions and answers aim to provide insights into current and future building thermal resilience research and applications, and more importantly to inspire new significant questions in the field.

This paper presents new results on the link between energy factors and commercial mortgage default. First, we summarize results from an empirical analysis of the impact of source energy use intensity (EUI) and electricity prices on mortgage default. We used a unique data set that merges building-level energy use data from city benchmarking ordinances and financial data for commercial mortgages on the same buildings. We found that building source EUI and electricity price are statistically and economically associated with commercial mortgage defaults. Next, we present five case studies on the impact of energy use and price variations on default risk: three office buildings, a hotel, and a multi-family residential building. We use the empirical model coefficients to compute the default risk impacts due to variations in source EUI and electricity price over the course of the mortgage term. We found that variations in source EUI could raise or lower the default rates in these properties by between 5% and 40%, depending on the property type and geography. Electricity pricing has an even greater effect – roughly 60% change in default rate in the Denver area and nearly 90% in northern California. Finally, we propose an energy risk score that lenders can use to assess energy risk and inform mortgage terms. The score is being developed and piloted in collaboration with three mortgage lenders. We conclude with implications of this score as a market signal and mechanism for incentivizing energy efficiency investments through the commercial mortgage channel.

The small commercial office market experiences a number of unique barriers to achieving substantial energy reductions. Those barriers include: 1) a lack of awareness of and access to centralized, comprehensive, cost-evaluative information about how to achieve energy targets, and 2) affordable access to energy reduction services such as engineering and auditing services. Energy efficiency tools and services currently involve higher costs on a per-square-foot or kilowatt-hour (kWh)-saved basis. Small commercial buildings are consequently underserved by the energy services market and disadvantaged in accessing the same detailed retrofit information that larger buildings realize. Meeting national and state energy goals requires whole-building integrated solutions that enable this sector to realize deep energy savings successfully and affordably. This project developed cost-effective retrofit packages of commercial, whole-building integrated systems to achieve zero net energy (ZNE) performance for California multi-story small commercial offices. The energy efficiency measure (EEM) packages target 50% energy savings over existing energy use, with costs within 10% of conventional-performance construction costs, and internal rates of return (IRR) of 5% or more. The developed packages of EEMs are validated for energy performance, as well as visual and thermal comfort, under both controlled laboratory test conditions for emerging technologies and demonstrated in an occupied small commercial building. Validated EEMs were built into an online public software platform that enables small commercial facilities to conduct whole-building retrofit assessments, relevant to their building, providing cost-evaluative feedback on EEMs and packages of measures.

Extreme hot and cold weather events are becoming more frequent, intense, and longer due to climate change. When these events occur coincidentally with power outages, the resulting extreme indoor temperatures pose a severe health hazard for occupants. This study conducted a holistic modeling and analysis of an assisted living facility, where senior residents live, to assess its thermal resilience performance under a six-day heat wave in 2015 and a three-day cold snap in 2021 with power outages. Impacts of 13 energy efficiency measures on thermal resilience and backup power capacity of the facility were evaluated. Three thermal resilience metrics: the SET (standard effective temperature) degree-hours, the Heat Index, and the Hours of Safety, were used and calculated from the EnergyPlus simulation models. Major findings are: (1) the facility would suffer from extreme temperatures during the cold and hot events without a power supply, not meeting the passive survivability requirements; (2) most passive envelope measures improve thermal resilience for both hot and cold events, but making the building envelope airtight results in conflicting performance between the hot and cold events; (3) natural ventilation is an effective measure to mitigate summer indoor overheating; and (4) the energy efficiency package can reduce backup power capacity by 19% for the three-day cold snap. It is recommended that building technologies and design strategies be evaluated to consider co-benefits of energy use, thermal resilience, and backup power needs through building energy codes or policies for existing and new buildings, which are transitioning for decarbonization and climate resilience.

Occupant behavior (OB) is one of the significant sources of uncertainty in building performance simulation. While OB modeling has received increased attention in the past decade, research on the degree of granularity or level of detail (LoD) required for representing occupants is still in the nascent stages. This paper analyzes the modeling and applicability of three LoDs to represent occupants in building performance assessment. A medium-sized prototype office building located in Chicago, Illinois is used as the simulation case study. Ten occupant-centric attributes are adopted to develop the LoDs for OB representation. We first demonstrate the different modeling approaches required for simulating the three fidelity levels. Later, we illustrate the suitability of the developed LoDs in supporting six building performance use cases across different lifecycle stages. This study intends to provide guidance for the building simulation community on appropriate OB representation to support various use cases.

Rapid electrification of buildings at the district scale is needed for cities to achieve climate change mitigation goals. However, most electrification studies focus on either the single building level or the city/region building stock level, and depend on the slow and uncertain process of requesting personally identifiable customer energy usage data from utilities. To answer a key question facing local policymakers: “Where can electrification proceed at scale without first upgrading the grid?” this study aims to quantify and inform building electrification impacts at the district scale using detailed building energy modeling and based on public records datasets. We explore how energy efficiency retrofits can help mitigate increased peak electric demand, and quantify impacts to energy use and carbon emissions. Building energy models of a baseline, and scenarios of simple electrification, energy retrofits, and electrification in combination with retrofits were created and simulated for 54 commercial buildings in two contiguous districts of San Francisco. A simple electrification scenario increased annual electricity consumption but reduced annual site energy usage by 15% to 17%, mainly due to replacing inefficient gas furnaces and boilers with more efficient heat pumps. Peak demand increased 7.4% for Fisherman's Wharf (e.g. within the capacity of the existing power grid), while the Design District showed a marginal decrease. Annual carbon emissions were reduced by 46% and 37%. Combining electrification with efficiency upgrades reduced peak demand by 26% and 40%, and annual carbon emissions by 63% and 64% for the two districts. These results indicate that impacts of electrification depend on the mix of building uses within a district, and coupling electrification with energy efficiency upgrades is an effective strategy to decarbonize buildings while maintaining or reducing the peak electric demand.

Performance quantification through simulation has been particularly advantageous to building design, as it can be applied to non-existent buildings in the design process, allows for testing design variants under identical conditions, and demands much less resources as compared to physical measurements. Consequently, use of building simulation in the design process has evolved to – for example – establish and verify design performance, screen and optimize design parameters, and study robustness and adaptability in adverse conditions. In this context, the present chapter investigates how the state-of-the-art simulation-aided design procedures can contribute to realize occupant-centric design objectives. To this end, the chapter, first, discusses the ways in which simulation-aided design methods can represent occupants and capture their interactions with buildings' environmental control systems. Subsequently, a number of key simulation-aided design methods and objectives are explored with a focus on the role of occupants. Finally, a carefully described prototypical building model serves to demonstrate and test the introduced occupant-centric simulation-aided design procedures.

Fang Wang,1,* Feng-Hua Wang,2,3,* Kaiyu Sun,4,* Chang Jiang,2,5 Sui Peng,6 Li-Xia Xu,1 Ming Kuang,7 Gui-Fang Guo,2,5,8 Shu-Ling Chen9 1Department of Oncology, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2State Key Laboratory of Oncology in South China, The Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 3Department of Oncology, The Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 4Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 5Collaborative Innovation Center for Cancer Medicine, The Sun Yat-Sen University Cancer Center, Guangzhou, People’s Republic of China; 6Institute of Precision Medicine, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China; 7Department of Liver Surgery, Center of Hepato-Pancreato-Biliary Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, People’s Republic of China; 8VIP Department, Sun Yat-sen University Cancer Center, Guangzhou, People’s Republic of China; 9Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Shu-Ling Chen; Gui-Fang Guo, Email chenshling@mail.sysu.edu.cn; guogf@sysucc.org.cnIntroduction: Advanced biliary tract carcinoma (BTC) has a poor prognosis and few treatment options. We compared the efficacy of the PD-1 monoclonal antibody (PD-1-mAb) combined regimens with the standard chemotherapy in the first-line and second-line treatment of advanced BTC.Methods: We retrospectively assessed the patients with advanced BTC, who received treatment at the First Affiliated Hospital of Sun Yat-Sen University and the Sun Yat-Sen University Cancer Center. The patients were treat

The small commercial office market experiences a number of unique barriers to achieving substantial energy reductions. Those barriers include: 1) a lack of awareness of and access to centralized, comprehensive, cost-evaluative information about how to achieve energy targets, and 2) affordable access to energy reduction services such as engineering and auditing services. Energy efficiency tools and services currently involve higher costs on a per-square-foot or kilowatt-hour (kWh)-saved basis. Small commercial buildings are consequently underserved by the energy services market and disadvantaged in accessing the same detailed retrofit information that larger buildings realize. Meeting national and state energy goals requires whole-building integrated solutions that enable this sector to realize deep energy savings successfully and affordably. This project developed cost-effective retrofit packages of commercial, whole-building integrated systems to achieve zero net energy (ZNE) performance for California multi-story small commercial offices. The energy efficiency measure (EEM) packages target 50% energy savings over existing energy use, with costs within 10% of conventional-performance construction costs, and internal rates of return (IRR) of 5% or more. The developed packages of EEMs are validated for energy performance, as well as visual and thermal comfort, under both controlled laboratory test conditions for emerging technologies and demonstrated in an occupied small commercial building. Validated EEMs were built into an online public software platform that enables small commercial facilities to conduct whole-building retrofit assessments, relevant to their building, providing cost-evaluative feedback on EEMs and packages of measures.

This paper presents new results on the link between energy factors and commercial mortgage default. First, we summarize results from an empirical analysis of the impact of source energy use intensity (EUI) and electricity prices on mortgage default. We used a unique data set that merges building-level energy use data from city benchmarking ordinances and financial data for commercial mortgages on the same buildings. We found that building source EUI and electricity price are statistically and economically associated with commercial mortgage defaults. Next, we present five case studies on the impact of energy use and price variations on default risk: three office buildings, a hotel, and a multi-family residential building. We use the empirical model coefficients to compute the default risk impacts due to variations in source EUI and electricity price over the course of the mortgage term. We found that variations in source EUI could raise or lower the default rates in these properties by between 5% and 40%, depending on the property type and geography. Electricity pricing has an even greater effect – roughly 60% change in default rate in the Denver area and nearly 90% in northern California. Finally, we propose an energy risk score that lenders can use to assess energy risk and inform mortgage terms. The score is being developed and piloted in collaboration with three mortgage lenders. We conclude with implications of this score as a market signal and mechanism for incentivizing energy efficiency investments through the commercial mortgage channel.

Nowadays, large open spaces are popular in office buildings. However, occupants often complain about too cold and/or too warm in large open spaces. It remains a challenge to control the operation of air-conditioning systems to provide occupant comfort due to the ununiform distribution of airflow, internal heat gains and occupancy. Previous studies using CFD tools or building energy modelling tools alone did not solve the combined problem of the distributive temperature field in the space and the cooling demand from multiple terminal units. This study proposed to divide the large space into multiple subzone areas based on the layout of the terminal cooling equipment and the distribution of internal heat gains and occupancy. Regarding the uneven thermal environment among subzones, a coupling of FLUENT with EnergyPlus is used to compute the optimal thermostat setpoint for each subzone to ensure uniform occupant comfort in the large space. EnergyPlus computes the interior wall surface temperatures and terminal unit supply air flowrate of each subzone, which are passed to the CFD simulations as boundary conditions; while FLUENT simulates the temperature and PMV field with user defined function (UDF), as well as airflow rates across the virtual partition walls between two adjacent subzones, which are passed to EnergyPlus for consideration as inter-zone airflow. A case study using open office space in Hong Kong is conducted to demonstrate the validity of the methodology. Different temperature setpoints were estimated for the subzones, and the results indicated the subzone-based temperature setpoint could achieve uniform occupant thermal comfort and avoid overcooling. In addition, the coupling method improves the prediction accuracy of cooling energy than the standalone EnergyPlus program. Therefore, the coupling method can effectively provide a thermally comfortable environment with less energy use in large open office served by multiple terminal units.

Nowadays, large open spaces are popular in office buildings. However, occupants often complain about too cold and/or too warm in large open spaces. It remains a challenge to control the operation of air-conditioning systems to provide occupant comfort due to the ununiform distribution of airflow, internal heat gains and occupancy. Previous studies using CFD tools or building energy modelling tools alone did not solve the combined problem of the distributive temperature field in the space and the cooling demand from multiple terminal units. This study proposed to divide the large space into multiple subzone areas based on the layout of the terminal cooling equipment and the distribution of internal heat gains and occupancy. Regarding the uneven thermal environment among subzones, a coupling of FLUENT with EnergyPlus is used to compute the optimal thermostat setpoint for each subzone to ensure uniform occupant comfort in the large space. EnergyPlus computes the interior wall surface temperatures and terminal unit supply air flowrate of each subzone, which are passed to the CFD simulations as boundary conditions; while FLUENT simulates the temperature and PMV field with user defined function (UDF), as well as airflow rates across the virtual partition walls between two adjacent subzones, which are passed to EnergyPlus for consideration as inter-zone airflow. A case study using open office space in Hong Kong is conducted to demonstrate the validity of the methodology. Different temperature setpoints were estimated for the subzones, and the results indicated the subzone-based temperature setpoint could achieve uniform occupant thermal comfort and avoid overcooling. In addition, the coupling method improves the prediction accuracy of cooling energy than the standalone EnergyPlus program. Therefore, the coupling method can effectively provide a thermally comfortable environment with less energy use in large open office served by multiple terminal units.

Nowadays, large open spaces are popular in office buildings. However, occupants often complain about too cold and/or too warm in large open spaces. It remains a challenge to control the operation of air-conditioning systems to provide occupant comfort due to the ununiform distribution of airflow, internal heat gains and occupancy. Previous studies using CFD tools or building energy modelling tools alone did not solve the combined problem of the distributive temperature field in the space and the cooling demand from multiple terminal units. This study proposed to divide the large space into multiple subzone areas based on the layout of the terminal cooling equipment and the distribution of internal heat gains and occupancy. Regarding the uneven thermal environment among subzones, a coupling of FLUENT with EnergyPlus is used to compute the optimal thermostat setpoint for each subzone to ensure uniform occupant comfort in the large space. EnergyPlus computes the interior wall surface temperatures and terminal unit supply air flowrate of each subzone, which are passed to the CFD simulations as boundary conditions; while FLUENT simulates the temperature and PMV field with user defined function (UDF), as well as airflow rates across the virtual partition walls between two adjacent subzones, which are passed to EnergyPlus for consideration as inter-zone airflow. A case study using open office space in Hong Kong is conducted to demonstrate the validity of the methodology. Different temperature setpoints were estimated for the subzones, and the results indicated the subzone-based temperature setpoint could achieve uniform occupant thermal comfort and avoid overcooling. In addition, the coupling method improves the prediction accuracy of cooling energy than the standalone EnergyPlus program. Therefore, the coupling method can effectively provide a thermally comfortable environment with less energy use in large open office served by multiple terminal units.

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.

Large amount of low-grade waste heat is discharged into the environment during industrial processes. This part of waste heat can be collected to serve district heating systems as an important heat source. In most studies of industrial waste heat recovery, the proposed system simulations were unsophisticated in terms of analyzing the real processes. For this reason, the tangency analysis has recently been proposed, and it has been found effective in conducting optimization analysis for direct-heat-exchange systems with multi-heat sources. However, in this study, it has been found that the tangency method has limitations in designing systems with heat pumps, and therefore the disadvantages of tangency analysis are suggested and discussed. Exergy analysis reveals that without considering additional exergy generated by heat pumps, the systems designed by tangency technology tend not be the optimal configuration when heat pumps are employed. In this study, the process optimization principles have been developed from the exergy analysis of heat recovery systems with heat pumps. The optimization principles and mean-heat-transfer-times index are proposed as the key point of process design. Based on the principles, two specific optimization methods in graphic expression are suggested. In the case studies, energy input decreased by more than 70%, which compares favorably with that of tangency analysis. © 2019 Elsevier Ltd

Large amount of low-grade waste heat is discharged into the environment during industrial processes. This part of waste heat can be collected to serve district heating systems as an important heat source. In most studies of industrial waste heat recovery, the proposed system simulations were unsophisticated in terms of analyzing the real processes. For this reason, the tangency analysis has recently been proposed, and it has been found effective in conducting optimization analysis for direct-heat-exchange systems with multi-heat sources. However, in this study, it has been found that the tangency method has limitations in designing systems with heat pumps, and therefore the disadvantages of tangency analysis are suggested and discussed. Exergy analysis reveals that without considering additional exergy generated by heat pumps, the systems designed by tangency technology tend not be the optimal configuration when heat pumps are employed. In this study, the process optimization principles have been developed from the exergy analysis of heat recovery systems with heat pumps. The optimization principles and mean-heat-transfer-times index are proposed as the key point of process design. Based on the principles, two specific optimization methods in graphic expression are suggested. In the case studies, energy input decreased by more than 70%, which compares favorably with that of tangency analysis. © 2019 Elsevier Ltd

The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires-by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviors by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires.

Large amount of low-grade waste heat is discharged into the environment during industrial processes. This part of waste heat can be collected to serve district heating systems as an important heat source. In most studies of industrial waste heat recovery, the proposed system simulations were unsophisticated in terms of analyzing the real processes. For this reason, the tangency analysis has recently been proposed, and it has been found effective in conducting optimization analysis for direct-heat-exchange systems with multi-heat sources. However, in this study, it has been found that the tangency method has limitations in designing systems with heat pumps, and therefore the disadvantages of tangency analysis are suggested and discussed. Exergy analysis reveals that without considering additional exergy generated by heat pumps, the systems designed by tangency technology tend not be the optimal configuration when heat pumps are employed. In this study, the process optimization principles have been developed from the exergy analysis of heat recovery systems with heat pumps. The optimization principles and mean-heat-transfer-times index are proposed as the key point of process design. Based on the principles, two specific optimization methods in graphic expression are suggested. In the case studies, energy input decreased by more than 70%, which compares favorably with that of tangency analysis. © 2019 Elsevier Ltd

Building model calibration is critical in bringing simulated energy use closer to the actual consumption. This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration processes. The pattern-based process contains four key steps: (1) running the original precalibrated energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern, weather and input parameter interactions; and (4) automatically tuning the calibration parameters and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit. The proof of success of the methodology was demonstrated using a case study of an office building located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill, original building model and weather data with outputs resulting in a calibrated model that more closely matched that of the actual building energy use profile. The novelty of the developed calibration methodology lies in linking parameter tuning with the underlying logic associated with bias pattern identification. Although there are some limitations to this approach, the pattern-based automated calibration methodology can be universally adopted as an alternative to manual or hierarchical calibration approaches.

Building model calibration is critical in bringing simulated energy use closer to the actual consumption. This paper presents a novel, automated model calibration approach that uses logic linking parameter tuning with bias pattern recognition to overcome some of the disadvantages associated with traditional calibration processes. The pattern-based process contains four key steps: (1) running the original precalibrated energy model to obtain monthly simulated electricity and gas use; (2) establishing a pattern bias, either Universal or Seasonal Bias, by comparing load shape patterns of simulated and actual monthly energy use; (3) using programmed logic to select which parameter to tune first based on bias pattern, weather and input parameter interactions; and (4) automatically tuning the calibration parameters and checking the progress using pattern-fit criteria. The automated calibration algorithm was implemented in the Commercial Building Energy Saver, a web-based building energy retrofit analysis toolkit. The proof of success of the methodology was demonstrated using a case study of an office building located in San Francisco. The case study inputs included the monthly electricity bill, monthly gas bill, original building model and weather data with outputs resulting in a calibrated model that more closely matched that of the actual building energy use profile. The novelty of the developed calibration methodology lies in linking parameter tuning with the underlying logic associated with bias pattern identification. Although there are some limitations to this approach, the pattern-based automated calibration methodology can be universally adopted as an alternative to manual or hierarchical calibration approaches.

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.

Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermal loads, leading to more efficient operations than other system types. This paper introduces a new model that simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Compared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has more component models based on physics and thus has significant innovations in: (1) enabling advanced controls, including variable evaporating and condensing temperatures in the indoor and outdoor units, and variable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailed refrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, and pipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and (4) improving the usability of the model by significantly reducing the number of user input performance curves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from field tests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP system performance, which is key to determining code compliance credits as well as utilities incentive for VRF technologies. ©

Small commercial buildings in the United States consume 47% of the total primary energy of the buildings sector. Retrofitting small and medium commercial buildings poses a huge challenge for owners because they usually lack the expertise and resources to identify and evaluate cost-effective energy retrofit strategies. This paper presents the Commercial Building Energy Saver (CBES), an energy retrofit analysis toolkit, which calculates the energy use of a building, identifies and evaluates retrofit measures in terms of energy savings, energy cost savings and payback. The CBES Toolkit includes a web app (APP) for end users and the CBES Application Programming Interface (API) for integrating CBES with other energy software tools. The toolkit provides a rich set of features including: (1) Energy Benchmarking providing an Energy Star score, (2) Load Shape Analysis to identify potential building operation improvements, (3) Preliminary Retrofit Analysis which uses a custom developed pre-simulated database and, (4) Detailed Retrofit Analysis which utilizes real-time EnergyPlus simulations. CBES includes 100 configurable energy conservation measures (ECMs) that encompass IAQ, technical performance and cost data, for assessing 7 different prototype buildings in 16 climate zones in California and 6 vintages. A case study of a small office building demonstrates the use of the toolkit for retrofit analysis. The development of CBES provides a new contribution to the field by providing a straightforward and uncomplicated decision making process for small and medium business owners, leveraging different levels of assessment dependent upon user background, preference and data availability.

Variable refrigerant flow (VRF) systems vary the refrigerant flow to meet the dynamic zone thermal loads, leading to more efficient operations than other system types. This paper introduces a new model that simulates the energy performance of VRF systems in the heat pump (HP) operation mode. Compared with the current VRF-HP models implemented in EnergyPlus, the new VRF system model has more component models based on physics and thus has significant innovations in: (1) enabling advanced controls, including variable evaporating and condensing temperatures in the indoor and outdoor units, and variable fan speeds based on the temperature and zone load in the indoor units, (2) adding a detailed refrigerant pipe heat loss calculation using refrigerant flow rate, operational conditions, pipe length, and pipe insulation materials, (3) improving accuracy of simulation especially in partial load conditions, and (4) improving the usability of the model by significantly reducing the number of user input performance curves. The VRF-HP model is implemented in EnergyPlus and validated with measured data from field tests. Results show that the new VRF-HP model provides more accurate estimate of the VRF-HP system performance, which is key to determining code compliance credits as well as utilities incentive for VRF technologies. ©

This report summarizes the technical tasks and key findings of the integrated design for high performance buildings, a project funded under the U.S.-China Clean Energy Research Center for Building Energy Efficiency. Main outcomes include: (1) Integrated design, operations and occupant behavior during the building life cycle is the key to high performance and low energy buildings. Technologies alone do not guarantee low energy buildings. Best practice of operations and energy friendly occupant behavior play a significant role in energy performance of buildings. The integrated design workshop and case studies directly support the design and operations of five demonstration buildings in China. (2) The simulation framework improves the way we evaluate technologies andbuilding performance by considering various uncertainties and investment risks. Itcan be adopted in the building code and standards development, code compliancecalculations, and performance ratings. It provides a clear pathway to understandand assess the gap between expected performance during design and actualperformance during operations. (3) Data and information exchange across various disciplines of building design and operations are crucial to improve knowledge sharing and timely decision making. The preliminary research of the information exchange feeds into the development of results schema of EnergyPlus.

This report summarizes the technical tasks and key findings of the integrated design for high performance buildings, a project funded under the U.S.-China Clean Energy Research Center for Building Energy Efficiency. Main outcomes include: (1) Integrated design, operations and occupant behavior during the building life cycle is the key to high performance and low energy buildings. Technologies alone do not guarantee low energy buildings. Best practice of operations and energy friendly occupant behavior play a significant role in energy performance of buildings. The integrated design workshop and case studies directly support the design and operations of five demonstration buildings in China. (2) The simulation framework improves the way we evaluate technologies andbuilding performance by considering various uncertainties and investment risks. Itcan be adopted in the building code and standards development, code compliancecalculations, and performance ratings. It provides a clear pathway to understandand assess the gap between expected performance during design and actualperformance during operations. (3) Data and information exchange across various disciplines of building design and operations are crucial to improve knowledge sharing and timely decision making. The preliminary research of the information exchange feeds into the development of results schema of EnergyPlus.