AI Based Development and Prediction of Energy Prosumers

Energy prosumers, comprising individuals, businesses, or organizations, play a crucial role in modern energy systems by both consuming and generating electricity. This shift from traditional energy consumption models has led to increased energy imbalances, rising operational costs, and potential grid instability. Prosumers primarily utilize renewable energy sources, such as solar photovoltaic (SPV) systems, small wind turbine generators (SWTG), and micro-scale hydropower, while also relying on the grid when self-generation falls short. The rapid expansion of prosumer-based energy generation necessitates predictive models to enhance energy balance and cost optimization. This study develops an AI-driven energy prediction model to minimize prosumer energy imbalance costs. Historical weather and load data from Estonian energy consumers with solar installations serve as the basis for model training. The research integrates machine learning algorithms, including the Random Forest Regression model, to forecast energy generation and consumption patterns. Factors such as weather conditions, time of day, and user-specific characteristics are incorporated to enhance prediction accuracy. The results demonstrate an effective predictive model with an accuracy of 81.4%, showcasing its potential to encourage wider prosumer participation and support the adoption of renewable energy sources.