A Contextual Reinforcement Learning Approach for Electricity Consumption Forecasting in Buildings

Abstract

The energy management of buildings plays a vital role in the energy sector. With that in mind, and targeting an accurate forecast of electricity consumption, in the present paper is aimed to provide decision on the best prediction algorithm for each context. It may also increase energy usage related with renewables. In this way, the identification of different contexts is an advantage that may improve prediction accuracy. This paper proposes an innovative approach where a decision tree is used to identify different contexts in energy patterns. One week of five-minutes data sampling is used to test the proposed methodology. Each context is evaluated with a decision criterion based on reinforcement learning to find the best suitable forecasting algorithm. Two forecasting models are approached in this paper, based on K-Nearest Neighbor and Artificial Neural Networks, to illustrate the application of the proposed methodology. The reinforcement learning criterion consists of using the Multiarmed Bandit algorithm. The obtained results validate the adequacy of the proposed methodology in two case-studies: building; and industry.

Systematic Review on Deep Reinforcement Learning-Based Energy Management for Different Building Types

“…Furthermore, Ramos et al utilized RL to choose a suitable forecaster (DNN vs. KNN) at each time step, which is most likely to provide the best prediction based on different contexts. This is another form of adaptivity that can further reduce the prediction errors by utilizing RL [135]. Lie et al used DRL to directly forecast future energy consumption and reported improved performance over conventional supervised methods [136,137].…”

Building energy consumption prediction method based on Bayesian regression and thermal inertia correction

“…(Wang et al 2022) presents an adaptive probabilistic load forecasting model designed to autonomously generate high-performance neural network architectures tailored for diverse buildings, in order to improve the adaptivity and efficiency of the forecasting. (Ramos et al 2022) uses a decision tree to identify different contexts of energy patterns, which is evaluated with a reinforcementlearning-based decision criterion to develop the forecasting algorithm. (Syed et al 2021) proposes a hybrid deep learning model, which includes data cleaning stage and model building stage, to predict energy consumption for smart buildings. )…”