A Comparison of the Finest Electrical Energy Forecast Models

Predicting today's utilization of energy will determine how powerful it is in the future. Generators can change their output by foreseeing the demand for electricity. As a result, shortages or overproduction are prevented while resource consumption is optimized. These electrical services may be impacted by the season, the weather, volatility, and other occurrences. Therefore, it is essential for service providers to be able to foresee these situations and have a window of opportunity to take preventative action. The forecasting of electricity load could be demonstrated using a time series prediction problem. Many architectures can contribute in this field to build extremely accurate deep learning models for Short-term Load Forecasting (STLF) in electrical networks. In this paper, the used models are Random Forest, Transformer, Convolutional Neural Network, Linear Regression and Decision Trees. The supplier will choose among this models or combine them to realise an effective prediction.