Enhancing Microgrid Efficiency With Artificial Intelligence-Based Solar Energy Forecasting and Load Management

Effective forecasting methods to optimize the generation and consumption of solar energy are required because of the growing need for sustainable solutions. In its research, this endeavour makes use of advanced machine learning techniques: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) models in forecasting solar electricity production based on environmental factors like cloud cover, temperature, and seasonal variations. The research systematically processes data, derives features relevant to the problem at hand, and then tests the performance of the models with this rigorous testing using a dataset of 2,300 records collected from a 100 kW microgrid system. In all the results, the ANN outperforms the other two models of SVM and LSTM with its accuracy at 98.89 %, while precision and recall metrics underscore its ability to minimize misclassifications. Meanwhile, SVM and LSTM achieved accuracies of 95.60 % and 91.24 %, respectively, showing capabilities along with a level of inadequacy when compared with the ANN models. Consequently, the findings show that the ANN model can forecast solar energy generation proficiently but, in addition to that, gives quite a robust framework for real-time application. This research is of great importance, as its implications relate to optimizing load management in microgrid systems, improving energy efficiency, and supporting the integration of renewable energy sources.