Load prediction of microgrid optimal operation based on improved algorithm in machine learning

Abstract

Load prediction is an important problem in microgrid operation. In this study, the kernel extreme learning machine (KELM) algorithm in machine learning was used for load prediction. In order to improve the prediction accuracy, the KELM algorithm was improved by combining with Ant Colony Optimization (ACO) algorithm and Particle Swarm Optimization (PSO) algorithm, and then experiments were carried out on the property load of two communities in May-July. The experimental results showed that MAPE of the improved algorithm in predicting the load of the two communities was 1.43% and 1.57% respectively, and the operation time was 215 s and 223 s respectively, which were better than than the support vector machine (SVM) and KELM algorithms; the prediction results were close to the actual values, and the error changes were stable, which verified the effectiveness of the improved algorithm. The experimental results make some contributions to improve the accuracy of load prediction and promote the optimal operation of microgrid, which is conducive to the further development of microgrid.