Machine learning based novel ensemble learning framework for electricity operational forecasting.

• Novel ensemble framework which improves short-term load forecasting accuracy. • Model divides the problem into several subtasks based on peak and off-peak conditions. • Classification and regression models were developed to solve each subtask. • Framework is validated on real-world operational demand across regions in Australia. • Model has a substantially higher accuracy (up to 25.4% in MAE) over other methods. [Display omitted] To keep the balance between electricity demand and supply as well as infrastructure planning, it is important to accurately forecast the electricity demand. This has become a challenging task due to increasing share of renewable energy and prosumers (i.e. consumers who produce electricity) in the electricity grid. This paper develops a cooperative ensemble framework which divides the forecasting problem into several subtasks based on peak and off-peak conditions. Each subtask is then solved using multiple forecasting models that include classification and regression. The developed framework is finally validated on real-world operational demand across the National Electricity Market (NEM) of Australia. The performance is comprehensively compared against various state-of-the-art techniques in the field, which indicates up to 25.4% mean absolute error (MAE) improvement. [ABSTRACT FROM AUTHOR]