Analysis of Solar Power Generation Forecasting Using Machine Learning Techniques

Abstract

Abstract: Accurate solar power generation forecasting is critical for efficient grid integration and renewable energy management. This paper presents a comparative analysis of machine learning techniques for predicting solar power output using weather and historical generation data. We evaluate the performance of Naive Bayes and Artificial Neural Network (ANN) models trained on a curated dataset containing temperature, humidity, wind speed, and solar irradiance features. Our methodology emphasizes robust data preprocessing, including outlier removal, missing value imputation, and normalization, to enhance model reliability. Experimental results demonstrate that the ANN model achieves superior accuracy (RMSE: 0.18, R²: 0.92) compared to Naive Bayes (RMSE: 0.32, R²: 0.81) in day-ahead forecasts, attributed to its ability to capture non-linear relationships in solar irradiation patterns. The study also highlights the critical role of feature selection, with solar irradiance and temperature identified as the most influential predictors. These findings provide actionable insights for energy operators seeking to optimize forecasting systems for grid stability and renewable energy utilization