Exploring Machine Learning Models for Predictive Analytics in Solar Power Generation

Abstract

Solar power generation is a critical metric for energy management, grid stability, and renewable energy optimization. Predictive analytics offers promising solutions to improve generation efficiency through machine learning models that analyze environmental and generation data. This study focuses on developing machine learning algorithms to predict solar power generation accurately, addressing challenges like data variability, temporal alignment, and feature selection that influence energy forecasting and system reliability. The research employs supervised machine learning algorithms, including linear regression, random forests, and gradient boosting. Weather sensor data and generation records are preprocessed to align time-series data and identify key features influencing power output, which are then used to train and test the models. Evaluation metrics for the models include mean squared error (MSE), accuracy, and interpretability. Preliminary findings indicate that machine learning models, particularly Random Forests and Gradient Boosting, can effectively predict solar power generation with moderate to high accuracy, improving renewable energy management by optimizing grid stability. Random Forests emerged as the most reliable model, capturing non-linear relationships between variables such as ambient temperature and daily yield, while Gradient Boosting provided competitive performance but required more complex parameter tuning. Linear Regression, though less effective, highlighted opportunities for refining feature selection. The study emphasizes the importance of addressing data quality and variability through rigorous preprocessing and model validation. Findings underscore the predictive value of ambient temperature and daily yield in determining energy output, guiding strategies for efficient plant operation. By showcasing the integration of machine learning in renewable energy, this research highlights its potential to enhance solar power efficiency, optimize resource use, and contribute to more sustainable and resilient energy systems.