Novel data-driven models for forecasting Canadian electricity demand
With ever-increasing disruptions in supply chains throughout the world, like pandemics, political conflicts, and trade wars, power generation is becoming one of the major concerns of global and local economies. The recent increasing energy cost in Europe and North America is one of the main consequences and, at the same time, contributes to these disruptions. Therefore, electricity demand forecasting is crucial in power markets to increase cooperation and integration between players in a power grid. This study aims at reviewing the managerial implications of demand forecasting in the electricity supply chain. Also, some recent statistical and machine learning techniques for electricity demand forecasting used in the literature are analyzed and applied to Ontario's historical dataset. A descriptive analysis of electricity demand characteristics in Ontario, post, and pre-pandemic, is conducted. Furthermore, the forecasting performance of methods like dynamic regression, neural network autoregression, and prophet model are discussed and compared. Another contribution of this study is to include fuzzy hourly demand forecasts for a Canadian dataset.
electricity demand, demand forecasting, neural networks, dynamic regression