Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules

摘要: Abstract Building energy modeling provides a fundamental tool to evaluate the potential for efficiency contribute reducing world consumption and global emissions. Occupancy-related operations are key source of uncertainty building analysis, particularly aggregated stocks. At district or city level, it is critical estimate power load profiles sizing grid infrastructure, plant capacity allocation, measures. The stochastic nature behavior-related complicates creation models that accurately capture entire This research introduces new methodology called parametric schedules model occupancy-driven large diverse In contrast computationally expensive methodologies proposed in literature, our work does not use recursive time-consuming step. Occupancy estimated by extrapolation operation times directly from metered electric data; occupancy-related stochastically assigned each model, guaranteeing diversity stock. Our procedure has been tested on large, diversified data-set ∼ 25,000 commercial buildings Los Angeles, California. It proved be able adequately represent stock refine calibration process 1%. innovative approach represents useful asset utility companies, operators, urban planners, balancing authorities, seeking improve better impact conservation – part larger ComStock, which under development NREL.