A multi-objective predictive energy management strategy for residential grid-connected PV-battery hybrid systems based on machine learning technique

Abstract

This paper proposes a multi-objective predictive energy management strategy based on machine learning technique for residential grid-connected hybrid energy systems. The hybrid system considered in this study comprise three principal components: a photovoltaic array as a renewable energy source, a battery bank as an energy storage system, and residential building as an electric load. The proposed strategy comprises three levels of controls: a logical level to manage the computational load and accuracy, a dual prediction model based on residual causal dilated convolutional networks for energy production and electric load on system, and a multi-objective optimization for efficient trade of energy with the utility grid by battery charge scheduling. The prediction model used in this study can provide one-step ahead photovoltaic energy production and load forecast with sufficient accuracy using a sliding window training technique and can be implemented on an average personal computer. The energy management problem comprises multiple objectives that include minimization of energy bought from utility grid, maximization the battery bank’s state-of-charge and reduction of carbon dioxide emission. The optimization problem is constrained to the maximum allowed carbon dioxide production and battery bank’s state-of-charge limits. The proposed strategy is tested for static and dynamic electricity prices using hourly energy and load data. Simulation results show a high coefficient of determination of 93.08% for energy production predictions and 97.25% for electric load predictions using proposed dual prediction model. The proposed prediction model is benchmarked against naïve prediction, support vector machine and artificial neural network models using several metrics and shows noticeable improvements in prediction accuracy. Not only the proposed strategy combined with the proposed prediction model can handle over 50% of the total yearly load requirement but also shows a significant decrease in electricity bill and carbon dioxide compared to residential buildings without hybrid energy systems and hybrid energy system without energy management strategy.