Regression Learner Application Model-Based Short-Term Load Forecasting for Mascouche (Quebec, Canada)

Abstract

Load forecasting is crucial for power systems optimal operation and allows power utilities to overcome technical and economic issues. Some forecasting techniques are currently being deployed on a large scale to meet the requirements of increased energy demand while balancing it with the production to achieve socio-economic benefits for sustainable development. In this paper, we are diving into the forecasting using the regression method. We are focusing on short-term load forecasting and how it can give businesses valuable insights into future sales, labor needs, and more. Power utilities use short-term load forecasting technology to make reasonable power systems. A forecasting model with low prediction errors helps reduce operating costs and risks for the operators leading to models’ optimization. To make things real, we are using actual load and weather data from the Hydro-Quebec database. We will be exploring the capabilities, advantages, and limitations of this method, all while keeping an eye on the changing landscape of electricity supply and demand. Our study is centered around the Mascouche region in Quebec, Canada, where the load fluctuates between 60 to 140 megawatts.