A Neural Network Toolbox for Electricity Consumption Forecasting

Jarosław Protasiewicz

2020 W: International Joint Conference on Neural Networks (IJCNN) / Imran Razzak, Pushpak Bhattacharyya, Antonella Santone, Xiuwen Liu, J. Parker Mitchell et all ; Piscataway: IEEE, s. 1-6

2020 International Joint Conference on Neural Networks. Glasgow, 2020-07-19 - 2020-07-24

The aim of this study is to propose a neural toolbox for electricity consumption prediction. The toolkit covers the implementation of three artificial neural networks, namely: (i) a multi-layered perceptron network; (ii) a radial basis function network; and (iii) a self-organising map. Moreover, the toolbox includes tools known as metamodels, which allow easy use of these networks for forecasting. There are two prediction systems, namely: (i) serially connected models; and (ii) a two-levelled structure containing a classifier and a set of parallel models. They have been validated experimentally in the task of electricity consumption prediction. The results and flexibility of use suggest that the neural toolbox should help users to develop prediction systems of electricity consumption more conveniently, as it is designed for that particular purpose. The toolbox has been developed as open source – no commercial software is required to use it.