A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

Wu, Wenbo; Dong, Bing; Wang, Qi; Kong, Min; Yan, Da; An, Jingjing; Liu, Yapan

doi:10.1016/j.apenergy.2020.115656

Cited by 42 publications

(7 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recently, web-based and digital approaches were integrated with UBEMs for data collection and simulation. For example, Wu et al [36] used mobile position data to collect more representative occupancy-related information, compared it with the Department of Energy (DOE) reference models, and then evaluated the impacts on building energy consumption. The study observed a considerable difference in HVAC energy demand from variations in occupancy rate, although lighting and plug loads were not considered.…”

Section: Ubems and Energy Simulationmentioning

confidence: 99%

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Hossain,

Cicco,

Bilec

2024

Buildings

View full text Add to dashboard Cite

Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

show abstract

Section: Ubems and Energy Simulationmentioning

confidence: 99%

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Hossain,

Cicco,

Bilec

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…This prototype building model was created conforming to the ASHRAE Standard 90.1-2019 [36] . The model has been widely used in various studies to analyze building energy performance [37] , [38] . The outlook of the target building is depicted in Figure 3 .…”

Section: Figurementioning

confidence: 99%

Optimization-informed rule extraction for HVAC system: A case study of dedicated outdoor air system control in a mixed-humid climate zone

Choi

O’Neill

et al. 2023

Energy and Buildings

View full text Add to dashboard Cite

“…Occupantbased interdependencies are caused by human mobility among buildings. Regarding this, existing studies have discussed human-mobility-based geotagged social media data or mobile data [48]. Instead of directly considering all types of interdependencies, this paper focuses on the interdependency caused by building shadows and solar radiation and validates the impact of these interdependencies on the accuracy of the data-driven UBEM in forecasting.…”

Section: A Building Interdependency Analysismentioning

confidence: 99%

Times series forecasting for urban building energy consumption based on graph convolutional network

Cheng

Wang

et al. 2022

Applied Energy

View full text Add to dashboard Cite

A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption

Cited by 42 publications

References 48 publications

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Optimization-informed rule extraction for HVAC system: A case study of dedicated outdoor air system control in a mixed-humid climate zone

Times series forecasting for urban building energy consumption based on graph convolutional network

Contact Info

Product

Resources

About