Engineering topics
Hong, Tianzhen (ORCID:0000000318869137)
Publications and source records attributed to Hong, Tianzhen (ORCID:0000000318869137).
Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control
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Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment
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Assessment of energy and thermal resilience performance to inform climate mitigation of multifamily buildings in disadvantaged communities
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Ten questions concerning thermal resilience of buildings and occupants for climate adaptation
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A semantic ontology for representing and quantifying energy flexibility of buildings
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A three-year dataset supporting research on building energy management and occupancy analytics
Abstract This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m 2 ) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications—building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls—to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.
Anthropogenic heating of the urban environment: An investigation of feedback dynamics between urban micro-climate and decomposed anthropogenic heating from buildings
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A synthetic building operation dataset
Abstract This paper presents a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years’ historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants’ diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.
Informing the planning of rotating power outages in heat waves through data analytics of connected smart thermostats for residential buildings
Abstract With climate change, heat waves have become more frequent and intense. Rotating power outages happen when the power supply is unable to meet the cooling demand increase resulting from extreme high temperatures. Power outages during heat waves expose residents to high risks of overheating. In this study, we propose a novel data-driven inverse modelling approach to inform decision makers and grid operators on planning rotating power outages. We first infer the building thermal characteristics using the connected smart thermostat data, and used the estimated thermal dynamics to simulate the thermal resilience during a heat wave event. Our proposed method was tested for the California power outage in August 2020 by using the open source Ecobee Donate Your Data dataset. We found in California the power outage should not last more than two hours during heat waves to avoid overheating risks. Informing the residents in advance so they can prepare for it through pre-cooling is a simple but effective strategy to expand the acceptable power outage duration. In addition to assisting power outage planning, the proposed method can be used for other applications, such as to evaluate a building energy efficiency policy, to examine fuel poverty, and to estimate the load shifting potential of building stocks.
AlphaBuilding - Synthetic Buildings Operation Dataset
This is a synthetic building operation dataset which includes HVAC, lighting, miscellaneous electric loads (MELs) system operating conditions, occupant counts, environmental parameters, end-use and whole-building energy consumptions at 10-minute intervals. The data is created with 1395 annual simulations using the U.S. DOE detailed medium-sized reference office building, and 30 years' historical weather data in three typical climates including Miami, San Francisco, and Chicago. Three energy efficiency levels of the building and systems are considered. Assumptions regarding occupant movements, occupants' diverse temperature preferences, lighting, and MELs are adopted to reflect realistic building operations. A semantic building metadata schema - BRICK, is used to store the building metadata. The dataset is saved in a 1.2 TB of compressed HDF5 file. This dataset can be used in various applications, including building energy and load shape benchmarking, energy model calibration, evaluation of occupant and weather variability and their influences on building performance, algorithm development and testing for thermal and energy load prediction, model predictive control, policy development for reinforcement learning based building controls.
Modeling Thermal Interactions between Buildings in an Urban Context
Thermal interactions through longwave radiation exchange between buildings, especially in a dense urban environment, can strongly influence a building’s energy use and environmental impact. However, these interactions are either neglected or oversimplified in urban building energy modeling. We developed a new feature in EnergyPlus to explicitly consider this term in the surface heat balance calculations and developed an algorithm to batch calculating the surrounding surfaces’ view factors using a ray-tracing technique. We conducted a case study with a district in the Chicago downtown area to evaluate the longwave radiant heat exchange effects between urban buildings. Results show that the impact of the longwave radiant effects on annual energy use ranges from 0.1% to 3.3% increase for cooling and 0.3% to 3.6% decrease for heating, varying among individual buildings. At the district level, the total energy demand increases by 1.39% for cooling and decreases 0.45% for heating. We also observe the longwave radiation can increase the exterior surface temperature by up to 10 °C for certain exterior surfaces. These findings justify a detailed and accurate way to consider the thermal interactions between buildings in an urban context to inform urban planning and design.