Short term electric load forecasting via fuzzy neural collaboration


An important element of effective power system operation is the well-planned short term scheduling of power generating units. Power system operators use historical load data to schedule available generating units to meet hourly system loads in an economical and reliable manner. This paper describes how a Fuzzy Logic (FL) expert system is integrated with Artificial Neural Networks (ANN) for a more accurate short-term load forecast. The 24 h ahead forecasted load is obtained through two steps. First, a FL module maps the highly nonlinear relationship between the weather parameters and their impact on the daily electric load peak. Second, 12 ANN modules are trained using historical hourly load and weather data combined with the FL output data, to perform the final forecast. Comparisons made between this model, an ANN model, and an Autoregressive Moving Average (ARMA) model show the efficiency and accuracy of this new approach.

Document Type





short term load forecasting, fuzzy logic, neural networks

Publication Date


Recommended Citation

Tamimi, Mohammad, and Robert Egbert. "Short term electric load forecasting via fuzzy neural collaboration." Electric Power Systems Research 56, no. 3 (2000): 243-248.

Journal Title

Electric Power Systems Research