Results of Egyptian unified grid hourly load forecasting using an artificial neural network with expert system interface |
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Affiliation: | 1. Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran;2. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam;3. Department of Energy Technology, Aalborg University, Aalborg, Denmark;1. National Renewable Energy Laboratory, 15013 Denver West Pkwy, Golden CO 80401, USA;2. Colorado School of Mines, Golden CO 80401, USA |
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Abstract: | This paper presents the hourly load forecasting results of the Egyptian unified grid (EUG). The technique is based on a generalized model combining the features of ANN and an expert system. The above methodology makes the technique robust, updatable and provides for operator intervention when necessary. This property makes it especially suitable for the EUG where the load patterns are influenced mostly because of social activities, and weather contributes very little to load forecast. For example, many social occasions depend on religious preferences which cannot be decided well in advance.This technique has been tested with one year data of EUG during 1993. The results clearly demonstrate the advantage of the above methodology over statistical based techniques. The average absolute forecast errors for the proposed methodology is 2.63% with a standard deviation of 2.62% whereas, the conventional multiple regression method scores an average absolute error of 4.69% with a standard deviation of 4.03%. |
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