• Energy Research

Abstract Climate change and increased electrification of space and water heating in buildings can significantly affect future electricity demand and hourly demand profiles, which has implications for electric grid greenhouse gas emissions and capacity requirements. We use EnergyPlus to quantify building energy demand under historical and under several climate change projections of 32 kinds of building prototypes in 16 different climate zones of California and imposed these impacts on a year 2050 electric grid configuration by simulation in the Holistic Grid Resource Integration and Deployment (HIGRID) model. We find that climate change only prompted modest increases in grid resource capacity and negligible difference in greenhouse gas emissions since the additional electric load generally occurred during times with available renewable generation. Heating electrification, however, prompted a 30–40% reduction in greenhouse gas emissions but required significant grid resource capacity increases, due to the higher magnitude of load increases and lack of readily available renewable generation during the times when electrified heating loads occurred. Overall, this study translates climate change and electrification impacts to system-wide endpoint impacts on future electric grid configurations and highlights the complexities associated with translating building-level impacts to electric system-wide impacts.

Abstract Disadvantaged communities face a growing threat to staying safe during heat waves, especially during coincident power outages. This study develops a methodology to evaluate the effectiveness of passive cooling measures (those that operate without power) to improve residential building heat resilience. Building performance is simulated for representative homes and on district scales in two disadvantaged communities in Fresno, California. Eleven passive measures are evaluated using four heat resilience metrics with and without grid power. Results show performance of the mitigation measures varies by building characteristics, surrounding environment, and power scenario. The two most effective measures were installing solar-control window films and adding roof insulation. For pre-1978 single-family homes, these two measures can reduce unmet degree-hours (UDH) indoors by 12% and 11% respectively without grid power, or 28% and 37% with grid power. Their respective UDH reductions at district scale typically range 8% — 20% and 4% — 12% without grid power, or 14% — 44% and 8% — 51% with grid power. Top floors have higher overheating risk than lower floors during extreme heat events with coincident power outages. Natural ventilation can help, reducing UDH by 21% — 26%. The methodology and findings from this study can help cities, communities, and utilities develop effective and targeted strategies to promote greater residential heat resilience.

Variable air volume (VAV) and variable refrigerant flow (VRF) systems are widely used in office buildings. This study investigated VAV and VRF systems in five typical office buildings in China, and compared their cooling energy use. Site survey and field measurements were conducted to collect the data of building characteristics and operation. Measured cooling electricity use was collected from sub-metering in the five buildings. The sub-metering data normalized by climate and operating hours indicated that the cooling energy consumed by VRF systems was up to 70% lower than that consumed by VAV systems. This was mainly because of the different operation modes of both system types that led to significantly fewer operating hours for the VRF systems. Building simulations were used to quantify the impact of operation modes of VRF and VAV systems on cooling loads. A prototype office building in China was used as the model. The simulation results showed that the VRF operation mode required much lower cooling load when compared to the VAV operation mode. For example, the cooling loads decreased by 42% in Hong Kong and 53% in Qingdao. The key findings include the following: the VRF systems operated in the part-time-part-space mode enabling occupants to turn on the air-conditioning only when needed and when the spaces were occupied. However, the VAV systems operated in the full-time-full-space mode limiting occupants’ control of operation. These findings provide insights into VRF systems operation and controls as well as their energy performance, which could help guide HVAC designers on system selection and building operators or facility managers on system operations to achieve low- or zero-net energy buildings.

Abstract 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.

Abstract Building control is a challenging task, not least because of complex building dynamics ad multiple control objectives that are often conflicting. To tackle this challenge, we explore an end-to-end deep reinforcement learning paradigm, which learns an optimal control strategy to reduce energy consumption and to enhance occupant comfort from the data of building-controller interactions. Because real-world control policies need to be interpretable and efficient in learning, this work makes the following key contributions: (1) we investigated a systematic approach to encode expert knowledge in reinforcement learning through “experience replay” and/or “expert policy guidance”; (2) we proposed to regulate the smoothness property of the neural network to penalize the erratic behavior, which is found to dramatically stabilize the learning process and lead to interpretable control laws; (3) we established a virtual testbed for building control by combining the state-of-the-art building energy simulator EnergyPlus with a python environment to provide a systematic evaluation and comparison platform, which will not only further our understanding of the strengths and weaknesses of existing building control algorithms, but also suggest directions for future research. We experimentally verified our proposed deep reinforcement learning paradigm on the virtual testbed in case studies, which demonstrated promising results.

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework's robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework's effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework's potential for broader applications in optimizing community-scale residential energy retrofits.

Building performance simulation has been adopted to support decision making in the building life cycle. An essential issue is to ensure a building energy simulation model can capture the reality and complexity of buildings and their systems in both the static characteristics and dynamic operations. Building energy model calibration is a technique that takes various types of measured performance data (e.g., energy use) and tunes key model parameters to match the simulated results with the actual measurements. This study performed an application and evaluation of an automated pattern-based calibration method on commercial building models that were generated based on characteristics of real buildings. A public building dataset that includes high-level building attributes (e.g., building type, vintage, total floor area, number of stories, zip code) of 111 buildings in San Francisco, California, USA, was used to generate building models in EnergyPlus. Monthly level energy use calibrations were then conducted by comparing building model results against the actual buildings’ monthly electricity and natural gas consumption. The results showed 57 out of 111 buildings were successfully calibrated against actual buildings, while the remaining buildings showed opportunities for future calibration improvements. Enhancements to the pattern-based model calibration method are identified to expand its use for: (1) central heating, ventilation and air conditioning (HVAC) systems with chillers, (2) space heating and hot water heating with electricity sources, (3) mixed-use building types, and (4) partially occupied buildings.

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.