Comparing Load Forecasting Models: A Case Study

The prediction of electrical demand is extremely important to power companies because it allows them to ensure that they have sufficient capacity to meet demand and, in some cases, to estimate the amount of supply that is required. This requirement has gained significance due to the deregulation of the power industry in numerous countries. We have applied different machine learning techniques to find a solution to this problem. This paper evaluates the short-term and long-term power demand profiles using machine learning algorithms. We trained the proposed model using data collected from KPCL Karnataka and compared it with other forecasting models. Measuring the performance of a trained model involves using mean percentage error and RMSE measurements. This research focuses on helping enterprises effectively manage energy production based on load demands, resulting in improved grid reliability.