Machine-learning-integrated load scheduling for peak electricity reduction

The scheduling of household electrical loads can contribute to a significant reduction in peak demand. This paper introduces a load scheduling scheme that integrates an SVM (Support Vector Machine) model for demand prediction. The experiment results confirm the strength of the proposed scheme, showing its ability to achieve the intended performance in consideration of the trade-off among peak reduction, temperature band violation, and switch count.