Short-term Nodal Electrical Load Forecasting with Artificial Neural Networks

Abstract

According to current trends in the development of electricity markets, distribution and transmission system operators must purchase electricity to cover their losses in the networks and the wholesale electricity market. By reducing the error in forecasting losses by 1%, this will reduce the cost of compensating for imbalances in the amount of 131.2 million per year, which will reduce tariffs for distribution and transmission of electricity. The study describes a comparative analysis of different architectures of artificial neural networks of deep learning for short-term forecasting of nodal electrical load. A comparison of the results of forecasting artificial neural network architectures and classical forecasting methods was performed. Data from the Northwestern region of the United States and Turkish power system were used. The results of the study show that neural networks of deep learning are superior to classical methods.