RESULTS OF EGYPTIAN UNIFIED GRID HOURLY LOAD FORECASTING USING AN ARTIFICIAL NEURAL-NETWORK WITH EXPERT-SYSTEM INTERFACE

This paper presents the hourly load forecasting results of the Egyptia n unified grid (EUG). The technique is based on a generalized model co mbining the features of ANN and an expert system. The above methodolog y makes the technique robust, updatable and provides for operator inte rvention when necessary. This property makes it especially suitable fo r the EUG where the load patterns are influenced mostly because of soc ial activities, and weather contributes very little to load forecast. For example, many social occasions depend on religious preferences whi ch cannot be decided well in advance. This technique has been tested w ith one year data of EUG during 1993. The results clearly demonstrate the advantage of the above methodology over statistical based techniqu es. 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% w ith a standard deviation of 4.03%. (C) 1996 Elsevier Science S.A.