 Non‐linear mixed‐effects models for time series forecasting of smart meter demandCameron Roach, Rob Hyndman and Souhaib Ben Taieb Journal of Forecasting, 2021, vol. 40, issue 6, 1118-1130 Abstract: Buildings are typically equipped with smart meters to measure electricity demand at regular intervals. Smart meter data for a single building have many uses, such as forecasting and assessing overall building performance. However, when data are available from multiple buildings, there are additional applications that are rarely explored. For instance, we can explore how different building characteristics influence energy demand. If each building is treated as a random effect and building characteristics are handled as fixed effects, a mixed‐effects model can be used to estimate how characteristics affect energy usage. In this paper, we demonstrate that producing 1‐day‐ahead demand predictions for 123 commercial office buildings using mixed models can improve forecasting accuracy. We experiment with random intercept, random intercept and slope and non‐linear mixed models. The predictive performance of the mixed‐effects models are tested against naive, linear and non‐linear benchmark models fitted to each building separately. This research justifies using mixed models to improve forecasting accuracy and to quantify changes in energy consumption under different building configuration scenarios. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:wly:jforec:v:40:y:2021:i:6:p:1118-1130 Access Statistics for this article Journal of Forecasting is currently edited by Derek W. Bunn More articles in Journal of Forecasting from John Wiley & Sons, Ltd. |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.