Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning

Abstract

Data-driven forecasting techniques have been widely used for building load forecasting due to their accuracy and wide availability of operational data. Recent advances have been underpinned by the increased capability of machine learning (ML) algorithms; however, most studies only tested ML techniques on a single or a small number of buildings over short periods, lacking reliable tests. Moreover, few studies focused on the effects of characteristics of building load profiles on forecast accuracy, lacking the interpretation of ML-based prediction results. In this study, we investigate the impacts of building load dispersion level on its best load forecasting accuracy, which is obtained by comparing the forecasting performances of 11 prediction models over 9 weeks for 56 British non-domestic buildings. We find that conventional shallow ML models still outperform the increasingly popular deep learning models for time-series load forecasting, and ensemble learning can help improve forecast accuracy by integrating diverse individual models. We demonstrate that each building’s best forecasting performance is largely influenced by the load dispersion level. In practice, the proposed dispersion metrics are recommended to quantify load dispersion levels before model development. For a building with a low dispersion level, the simple persistence model has satisfactory performance and could be directly used for design, control, and fault diagnosis of building energy systems for energy efficiency and energy flexibility.