Effect of Machine Learning and Artificial Intelligence on Solar Energy Balancing Costs by Enhancing Solar Power Prediction Accuracy

Abstract

The inherent intermittency and variability of solar power present significant economic and operational challenges to its widespread integration into modern electricity grids, primarily through the accrual of substantial "balancing costs". Accurate power prediction is essential to mitigate these costs, optimize grid reliability, and enable more effective energy trading strategies. This paper presents a comparative analysis of traditional machine learning and advanced deep learning models to enhance solar power output prediction accuracy. A comprehensive methodology was employed, utilizing historical meteorological and solar station data to train and evaluate models, including Linear Regression, Decision Tree, Random Forest, Support Vector Regressor (SVR), and an optimized Artificial Neural Network (ANN). The results demonstrate that the optimized ANN model significantly outperforms its traditional counterparts, achieving a root mean square error (RMSE) of 16.7984 and a coefficient of determination (R-squared) of 0.9680. This represents a 5.47% reduction in RMSE compared to the best-performing traditional model (Linear Regression). The findings validate that an AI-driven approach can capture the complex, nonlinear dependencies in solar power data, offering a transformative pathway to reduce integration costs, enhance grid stability, and accelerate the transition toward sustainable energy systems.