Building energy modeling provides a fundamental tool to assess the potential for energy efficiency to contribute to reducing world energy consumption and global emissions. Occupancy-related operations are a key source of uncertainty for building energy analysis, particularly for aggregated building stocks. At a district or city level, it is critical to estimate aggregated power load profiles for sizing power grid infrastructure, power plant capacity allocation, and energy efficiency measures. The stochastic nature of behavior-related operations complicates the creation of models that accurately capture building load profiles for entire building stocks. This research introduces a new methodology called parametric schedules to model occupancy-driven schedules for large and diverse building stocks. In contrast to computationally expensive methodologies proposed in the literature, our work does not use a recursive time-consuming step. Occupancy is estimated by the extrapolation of operation times directly from metered electric consumption data; occupancy-related schedules are stochastically assigned to each building model, guaranteeing diversity of operation times in the stock. Our procedure has been tested on a large, diversified data-set of 25,000 commercial buildings in Los Angeles, California. It proved to be able to adequately represent the stochastic schedules diversity of the stock and to refine the stock calibration process by 1%. This innovative approach represents a useful asset for utility companies, grid operators, urban planners, and balancing authorities, seeking to improve building stock modeling and better estimate the impact of energy conservation measures. – This work is part of a larger stock modeling tool called ComStock, which is under development by NREL.
Bianchi, Carlo, et al. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules." Applied Energy, vol. 276, Jul. 2020. https://doi.org/10.1016/j.apenergy.2020.115470
Bianchi, Carlo, Zhang, Liang, Goldwasser, David, Parker, Andrew, & Horsey, Henry (2020). Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules. Applied Energy, 276. https://doi.org/10.1016/j.apenergy.2020.115470
Bianchi, Carlo, Zhang, Liang, Goldwasser, David, et al., "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy 276 (2020), https://doi.org/10.1016/j.apenergy.2020.115470
@article{osti_1660214,
author = {Bianchi, Carlo and Zhang, Liang and Goldwasser, David and Parker, Andrew and Horsey, Henry},
title = {Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules},
annote = {Building energy modeling provides a fundamental tool to assess the potential for energy efficiency to contribute to reducing world energy consumption and global emissions. Occupancy-related operations are a key source of uncertainty for building energy analysis, particularly for aggregated building stocks. At a district or city level, it is critical to estimate aggregated power load profiles for sizing power grid infrastructure, power plant capacity allocation, and energy efficiency measures. The stochastic nature of behavior-related operations complicates the creation of models that accurately capture building load profiles for entire building stocks. This research introduces a new methodology called parametric schedules to model occupancy-driven schedules for large and diverse building stocks. In contrast to computationally expensive methodologies proposed in the literature, our work does not use a recursive time-consuming step. Occupancy is estimated by the extrapolation of operation times directly from metered electric consumption data; occupancy-related schedules are stochastically assigned to each building model, guaranteeing diversity of operation times in the stock. Our procedure has been tested on a large, diversified data-set of 25,000 commercial buildings in Los Angeles, California. It proved to be able to adequately represent the stochastic schedules diversity of the stock and to refine the stock calibration process by 1%. This innovative approach represents a useful asset for utility companies, grid operators, urban planners, and balancing authorities, seeking to improve building stock modeling and better estimate the impact of energy conservation measures. – This work is part of a larger stock modeling tool called ComStock, which is under development by NREL.},
doi = {10.1016/j.apenergy.2020.115470},
url = {https://www.osti.gov/biblio/1660214},
journal = {Applied Energy},
issn = {ISSN 0306-2619},
volume = {276},
place = {United States},
publisher = {Elsevier},
year = {2020},
month = {07}}
Ghanbari, Arash; Ghaderi, S. Farid; Azadeh, M. Ali
2nd International Conference on Computer and Automation Engineering (ICCAE 2010), 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE)https://doi.org/10.1109/ICCAE.2010.5451534