ANN Technique for the Prediction of Solar Power Generation

For effective energy management and the integration of renewable energy sources into the power system, accurate solar power projection is essential. This research provides a novel method for forecasting solar power generation using Artificial Neural Networks (ANN). To improve prediction accuracy, the suggested approach makes use of past solar power data, weather forecasts, and environmental factors. The ANN model is trained on a large dataset using a multi-layer perceptron (MLP) architecture, which enables it to recognize intricate non-linear correlations between input characteristics and solar power production. In comparison to other machine learning approaches and conventional statistical methods, the study assesses the performance of the ANN model. The results show that in terms of mean absolute error (MAE) and root mean squared error (RMSE), the ANN-based model performs noticeably better than existing approaches.