Enhancing Energy Market Price Forecasting through Advanced Machine Learning Techniques: Innovations, Challenges, Applications, and Future Perspectives

Price prediction and load forecasting hold significant importance for market participants, influencing bidding strategies, asset allocation, contract negotiations, risk hedging, and facility investment planning. Furthermore, market operators can leverage electricity price forecasts to anticipate market power indexes, facilitating the monitoring of participants' actions. The deregulation of power and energy industries in numerous countries has heightened the significance of addressing this necessity. Recent research endeavors have explored the application of neural networks, advanced machine learning (ML), and deep learning (DL) algorithms as potential solutions to enhance forecasting accuracy in this context. This paper explores and focuses on the use of various ML and DL techniques in load forecasting (LF) and energy price forecasting (EPF), highlighting the complexities involved and the potential of these technologies to revolutionize the energy sector. It emphasizes the fluctuating nature of renewable energy sources (RESs) and the need for balancing energy supply and demand. The research also addresses the impact of big data on smart energy networks and the growing importance of real-time load forecasting in the electricity market. It highlights the transformative role of AI in energy sector forecasting and the integration of renewable energy, considering both the technical and economic aspects of energy management.