A study on the short-term load forecasting expert system considering the load variations due to the change in temperature

기온변화에 의한 수요변동을 고려한 단기 전력수요예측 전문가시스템의 연구

In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.