A Supervised Machine Learning Approach to Control Energy Storage Devices

This paper introduces a supervised machine learning (ML) approach to predict and schedule the real-time operation mode of the next operation interval for residential PV/battery systems controlled by mode-based controllers. The performance of the mode-based economic model-predictive control approach is used as the benchmark. The residential load and PV data used in this paper are 1-min data downloaded from the Pecan Street Project website. The optimal operation mode for each control interval is first derived from the historical data used as the training set. Then, four ML algorithms (i.e., neural network, support vector machine, logistic regression, and random forest algorithms) are applied. We compared the performance of the four algorithms when using different number of features and length of the training sets extracted from different months of the year. Simulation results show that using the ML approach can effectively improve the performance of the mode-based control system and reduce the computation effort of local controllers because the training can be completed on a cloud-based ML engine. The work presented in this paper paves the way for using a shared-learning platform to design controllers of residential PV/storage systems. This may significantly reduce the cost for implementing such systems.