Predicting the Energy Consumption Patterns of Prosumers in Decentralized Energy Systems

Integrating decentralized energy systems is vital for incorporating renewable energy sources, particularly solar power, into the modern grid. This shift requires active engagement from prosumers—entities that produce and consume energy. However, the inherent variability in energy usage patterns and fluctuating weather conditions introduces significant grid stability and cost-effectiveness challenges, often resulting in production inconsistencies. Addressing these issues necessitates precise forecasting of energy consumption and production. This research explores the application of machine learning, specifically the Random Forest Regressor model, to enhance prediction accuracy in decentralized energy systems. Historical time-series data on solar irradiance and energy consumption were utilized to train the model, with performance assessed through K-fold cross-validation. The Random Forest Regressor achieved a mean absolute error (MAE) of 97.071 in energy level predictions, demonstrating substantial accuracy. A comparison between the model’s forecasts and actual values confirmed its effectiveness in capturing complex patterns in prosumer energy behavior. These findings indicate that machine learning, particularly Random Forest, can significantly improve grid stability and energy management in decentralized energy systems, facilitating a more resilient and efficient integration of renewable energy.