Additive Gaussian process prediction for electrical loads compared with deep learning models

Abstract

Probabilistic prediction for electrical loads receives more attention in recent years for leveraging big data and assessing diverse scenarios. Since the classical machine learning (ML) model as a 'blackbox' predictor cannot produce the probabilistic prediction directly, Gaussian process (GP) model appears to be an effective solution. This paper proposes an additive GP model for the short-term electrical load prediction, in which the characteristics of each feature can be encoded in the kernel. For comparison, we survey the literature and construct two deep learning models, quantile and ensemble deep neural networks (NNs), which can produce a probabilistic prediction. The results show the proposed additive GP model can outperform deep learning models, with the optimal kernel selection for features such as weather-related variables.