Load Predictions in Electrical Energy Network: Current Knowledge and Future Directions Using Machine Learning

The accurate prediction of electricity demand is becoming increasingly important in light of market deregulation and the integration ofrenewable energy sources into the electrical energy network. To address this challenge, enhanced models with intelligence are being developed to provide power of precise forecasts for multiple time horizons. The use of these forecasting algorithms is critical in smart grids, as they can effectively resolve uncertainty and aid in cost and energy efficiency decisions such as schedule generation, system reliability and power optimization, and economic grid operations. The prediction accuracy of these algorithms is highly sought after, as many power operator activities such as load dispatch depend on the accuracy of short- term forecasts. This article provides an overview of the current state of the art in intelligent forecasting algorithms and their applications in the electrical energy network.