Renewable Energy MicroGrid Power Forecasting: AI Techniques with Environmental Perspective

Abstract

This research focuses on the accurate prediction of renewable energy generation in microgrid systems using artificial intelligence (AI) techniques. The study compares and evaluates different AI models, including Artificial Neural Networks (ANN), Fuzzy Logic, and Adaptive Neuro-Fuzzy Inference Systems (ANFIS), for forecasting solar and wind power output. The models are trained and tested using real-world data, and their performance is assessed using metrics such as Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Sum of Squared Errors (SSE). The results demonstrate that the ANN model achieves the lowest RMSE and MAPE values for wind power prediction, while the Fuzzy Logic model performs well in predicting solar power generation. These findings indicate the effectiveness of AI techniques in accurately forecasting renewable energy output in microgrid systems. The proposed approach has implications for optimizing the utilization and integration of renewable energy sources, leading to more efficient and sustainable microgrid operations. Future research directions may involve exploring advanced deep-learning models and incorporating additional environmental factors to further enhance the accuracy and reliability of renewable energy power forecasting in microgrids.