A Multi-Scale Calibration Approach for Process-Oriented Aggregated Building Energy Demand Models

TitleA Multi-Scale Calibration Approach for Process-Oriented Aggregated Building Energy Demand Models
Publication TypeJournal Article
Year of Publication2019
JournalEnergy and Buildings
Volume191
Pages82-94
Date Published03/2019
Abstract / Summary

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL’s BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

URLhttp://dx.doi.org/10.1016/j.enbuild.2019.02.018
DOI10.1016/j.enbuild.2019.02.018
Funding Program: 
Journal: Energy and Buildings
Year of Publication: 2019
Volume: 191
Pages: 82-94
Date Published: 03/2019

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. We propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL’s BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

DOI: 10.1016/j.enbuild.2019.02.018
Citation:
Taylor, Z, Y Xie, C Burleyson, N Voisin, and I Kraucunas.  2019.  "A Multi-Scale Calibration Approach for Process-Oriented Aggregated Building Energy Demand Models."  Energy and Buildings 191: 82-94.  https://doi.org/10.1016/j.enbuild.2019.02.018.