Forecasting Building Energy Demand under Uncertainty Using Gaussian Process Regression: Feature Selection, Baseline Prediction, Parametric Analysis and a Web-based Tool

Abstract

Gaussian process (GP) regression has drawn growing attention in the application of building energy demand prediction. This paper demonstrates the process of developing GP models for baseline prediction and parametric analysis. In particular, this study proposes the method of feature selection based on characteristic lengthscale in covariance function, as well as the methods of anomaly detection and parametric analysis utilizing the predictive distribution of a GP regression. Two case studies are used to illustrate the processes in detail. The results show that successful feature selection can improve predictive accuracy and reduce computational cost. In baseline prediction, the outcome of a GP regression gives a confidence range, which provides valuable information for anomaly detection. In parametric analysis, the additional variance in the output caused by intentionally varying the range of a control variable gives an estimate of its impact on energy demand. Another contribution of this study is the development of a web-based tool, which allows users to build GP models without knowledge of the details GPs or programming skills.