ABSTRACT
Our research is into the area of energy management and load optimization in microgrid stations, specifically as EV charging station based microgrid Stations Thus, in this study, we have forecasted the loads to correctly use the resources with intelligent energy management algorithms using CNNs, KNNs, RNNs and SVMs whilst classifying and adopted SVM optimized ANN which predicts efficiently. The results demonstrate promising results, with CNN and KNN reaching above 98% accuracy for both. The models also show high accuracy in load prediction and classification. In particular, RNN SVM ANN are better than 90%. “We have shown the potential of machine learning to improve microgrid efficiencies and stability,” the authors concluded. With proper load forecasting, operators would be able to take informed decisions and do efficient resource allocation while balancing the peak loads which in turn will improve the user experience. This research provides a way forward for sustainable energy management and solutions to better enhance the performance of microgrid in the role of electric vehicle charging station.
