Machine Learning Techniques for Demand Forecasting in the Electricity Sector

This chapter presents a comprehensive review of machine learning (ML) techniques as applied to forecasting demand within the electricity sector—a pivotal factor in the optimization of energy systems. The recent influx of renewable energy sources and the advent of smart grid technologies have underscored the limitations of traditional forecasting methods. In response, ML offers an array of advanced, data-centric algorithms that adeptly capture complex patterns, enabling more precise forecasts of future electricity demand. The chapter progresses through an evaluation of fundamental ML methodologies pertinent to demand forecasting, spanning time-series models, regression analyses, ensemble learning, and state-of-the-art deep learning and hybrid algorithms. It assesses each methodology’s inherent advantages and constraints and furnishes an empirical perspective on their operational efficacy in the energy domain. This chapter showcases the application and analytical benefits of ML in actual forecasting scenarios, underscoring the enhanced accuracy and adaptability these methods bring to the fore. Future challenges, including data integrity, system integration, and scalability, are addressed, with a forward-looking analysis on the evolution of ML in tandem with the continuous transformation of energy networks. In addition, this chapter presents a case study where hybrid convolutional long-short term memory recurrent neural network has been applied to forecast electricity demand of Thailand’s metropolitan region showcasing the application of advanced ML algorithms in electricity sector’s demand forecasting area.