| Abstract: |
The electric utility planning process begins with the electric load forecasting, because of the advanced need for new utility plants. These long lead times require the utility planning horizon to be at least ten years long. Since utility decisions involve an economic analysis of the operating and investment costs, the utility planning horizon may range from fifteen to thirty years into the future. Forecasting load demand is a difficult procedure and combines art with science. The key contribution of forecasters is their knowledge of electricity consumers and an understanding of the way they use electricity and other competing energy forms. The problem gains special aspects in developing countries, such as Egypt, because of the high demand growth rate as well as the wide differences in the modes and levels of consumption in the various regions (governo rates) in the country. During the recent years, some new mathematical tools have been published such as expert system (EXP.), Artificial Neural Network (ANN) and Fuzzy logic systems. These tools almost replaced the classic methods used by most utilities and research centers personnel for forecasting. In this study, a technique based on the Artificial Neural Network (ANN) method, is used to estimate Peak load and Light load for the Egyptian power system network as an example for developing countries. This technique is highlighted by the accuracy and sensitivity of the model with respect to the ANN parameters. The proposed technique can thus be applied to simple as well as extended power system networks. Consequently, in this study, several structures for Neural Networks are proposed and tested. They proved to perform as one of the best and most sophisticated forecasting systems. In this study, the case of a number of neurons layers equal 7, gives the best results with high accuracy with the least error. The forecasted Peak loads and Light loads, up to year 2010, for the six Regions of the Egyptian Unified Network; Alexandria, Delta, Cairo, North Upper Egypt, South Upper Egypt and the Canal, are obtained directly from one case by using the actual and practical past ten years data.
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