AI-powered Prediction Drives Hungarian Renewable Energy Integration

This paper investigates the multifaceted role of Artificial Intelligence (AI) in achieving a high-renewable energy future for Hungary's power sector. Due to their intermittent nature, the present authors address the challenges associated with integrating renewable energy sources (RES), such as solar and wind power. The paper highlights how AI, particularly machine learning (ML) techniques, can significantly enhance the accuracy of renewable energy generation forecasting. A novel ML model is presented, combining Multilayer Perceptron (MLP), and Support Vector Regression (SVR) to predict energy consumption from renewable sources. This method emphasizes the importance of historical load data preprocessing and time series analysis for improved forecasting accuracy. Furthermore, the paper explores the potential of AI for real-time optimization and demand response management throughout the electricity value chain. By strategically integrating AI solutions, Hungary can overcome the challenges of high renewable energy penetration and pave the way for a more sustainable and efficient power grid.