End-to-end data-driven modeling framework for automated and trustworthy short-term building energy load forecasting

Abstract

Conventional automated machine learning (AutoML) technologies fall short in preprocessing low-quality raw data and adapting to varying indoor and outdoor environments, leading to accuracy reduction in forecasting short-term building energy loads. Moreover, their predictions are not transparent because of their black box nature. Hence, the building field currently lacks an AutoML framework capable of data quality enhancement, environment self-adaptation, and model interpretation. To address this research gap, an improved AutoML-based end-to-end data-driven modeling framework is proposed. Bayesian optimization is applied by this framework to find an optimal data preprocessing process for quality improvement of raw data. It bridges the gap where conventional AutoML technologies cannot automatically handle missing data and outliers. A sliding window-based model retraining strategy is utilized to achieve environment self-adaptation, contributing to the accuracy enhancement of AutoML technologies. Moreover, a local interpretable model-agnostic explanations-based approach is developed to interpret predictions made by the improved framework. It overcomes the poor interpretability of conventional AutoML technologies. The performance of the improved framework in forecasting one-hour ahead cooling loads is evaluated using two-year operational data from a real building. It is discovered that the accuracy of the improved framework increases by 4.24%–8.79% compared with four conventional frameworks for buildings with not only high-quality but also low-quality operational data. Furthermore, it is demonstrated that the developed model interpretation approach can effectively explain the predictions of the improved framework. The improved framework offers a novel perspective on creating accurate and reliable AutoML frameworks tailored to building energy load prediction tasks and other similar tasks.