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.
[1]
Thomas Martinetz,et al.
'Neural-gas' network for vector quantization and its application to time-series prediction
,
1993,
IEEE Trans. Neural Networks.
[2]
John Moody,et al.
Prediction Risk and Architecture Selection for Neural Networks
,
1994
.
[3]
A. J. Germond,et al.
Application of Artificial Neural Networks to Load Forecasting
,
1992
.
[4]
R. R. Hocking.
The analysis and selection of variables in linear regression
,
1976
.
[5]
Anders Krogh,et al.
Introduction to the theory of neural computation
,
1994,
The advanced book program.