A Python Application for Hourly Electricity Prices Forecasting Using Neural Networks

The paper considers the problem of forecasting hourly market electricity prices using the artificial neural networks. The existing publications are analyzed to determine the most popular methods of electricity prices forecasting, the types of neural networks most commonly used in forecasting, and the existing tools of forecasting algorithms implementation based on neural networks. A Python application has been developed to perform the electricity prices forecasting and analysis, using NeuroLab and Keras libraries for creating neural networks. The paper also presents the results of numerical experiments on electricity prices forecasting using the developed application. The experiments tested the forecast quality dependence on the library used, the structure of neural network, and the training algorithms used.