Accurate Solar Power Forecasting: A Comparative Study of machine Learning Techniques

Abstract

Effective real-time energy management is crucial for the transition to a sustainable and resilient energy system. Solar energy, characterized by its variability due to weather conditions, seasonality, and geographic location, requires reliable forecasting models to maximize its integration and efficient management within the grid. Anticipating energy consumption is also vital to avoid demand peaks and imbalances in electricity distribution. This study deals with machine learning techniques, such as random forest models, decision trees, and XG Boost for modeling and predicting solar energy production and energy consumption by comparing theirs ability to handle complex datasets, identify nonlinear relationships, and provide accurate forecasts.