Heterogeneous artificial neural network for short term electrical load forecasting

Short term electrical load forecasting is a topic of major interest for the planning of energy production and distribution. The use of artificial neural networks has been demonstrated as a valid alternative to classical statistical methods in terms of accuracy of results. However, a common architecture able to forecast the load in different geographical regions, showing different load shape and climate characteristics, is still missing. In this paper we discuss a heterogeneous neural network architecture composed of an unsupervised part, namely a neural gas, which is used to analyze the process in sub models finding local features in the data and suggesting regression variables, and a supervised one, a multilayer perceptron, which performs the approximation of the underlying function. The resulting outputs are then summed by a weighted fuzzy average, allowing a smooth transition between sub models. The effectiveness of the proposed architecture Is demonstrated by two days ahead load forecasting of L´Energie de L´Ouest Suisse (EOS) power system sub areas, corresponding to five different geographical regions, and of its total electrical load