β Scribed by M. Sforna; F. Proverbio
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This paper owes its origins to a project, still in progress at ENEL/ARC, which aims to investigate the application of artificial intelligence techniques and eventually to check their positive contribution in the field of short-term load forecasting. In particular, this article focuses on the construction problems of an integrated tool specifically designed to meet the needs of utility forecasters and power system operators. Even if the use of artificial neural networks for short-term forecasting had already been stressed in the past and no longer represents an innovative solution, the authors belie~,e that their use for online forecasting in an adaptive and reliable calculation structure still represents a new subject of interest.
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Short-term electric load forecasting is an important requirement for electric system operation. This paper employs a feed-forward neural network with a back-propagation algorithm for three types of short-term electric load forecasting: daily peak (valley) load, hourly load and the total load. The fo