Enhancing Energy Efficiency With AI: A Review of Machine Learning Models in Electricity Demand Forecasting

This study presents a comprehensive review of the impact of artificial intelligence (AI) and machine learning (ML) on enhancing energy efficiency, particularly in the context of electricity demand forecasting. The review systematically explores the paradigm shift brought about by the emergence of AI in energy efficiency, focusing on the role of AI in electricity demand forecasting and the historical evolution of forecasting techniques. A critical analysis of various ML models is conducted, examining their theoretical underpinnings, selection criteria, and performance in diverse scenarios. Key insights reveal that ML models, especially those incorporating deep learning and big data analytics, significantly outperform traditional forecasting methods in accuracy and adaptability. These models are adept at handling complex, nonlinear relationships and large datasets, making them particularly effective in the dynamic and increasingly renewable-focused energy markets. The review also highlights the importance of selecting appropriate ML models based on criteria such as accuracy, adaptability to forecasting periods, data handling capabilities, and environmental impact considerations. The study further delves into the technological, economic, and environmental impacts of ML in energy efficiency. It underscores the potential of ML to drive innov4eations in energy forecasting, contributing to more sustainable and efficient energy management. However, challenges such as data privacy, cybersecurity, and the need for skilled professionals are identified as critical areas requiring attention. Strategic recommendations are provided for practitioners and policymakers, emphasizing the need for investment in AI and ML training, development of supportive regulatory frameworks, and fostering collaborations across sectors. The review concludes with a future outlook, suggesting directions for future research in AI and energy efficiency, particularly in developing robust and scalable ML models that can integrate with renewable energy sources and smart grid technologies. This study serves as a valuable resource for researchers, practitioners, and policymakers engaged in the field of energy efficiency and AI-driven forecasting. Keywords: Machine Learning, Energy Efficiency, Demand Forecasting, Artificial Intelligence.