The application of artificial neural networks to substation load forecasting

The substation loading is highly correlated with the customers served. The substations in a distribution system can be categorized as residential, commercial and industrial. Each type has a different power consumption pattern. The substation loading will be varied according to the combination of the above three types of customers. In this paper, a supervisory functional artificial neural network (ANN) technique is applied to solve the load forecasting of three Taipower substations which serve the different customer types. The load forecasting accuracy is enhanced by considering the temperature effect on the substation load demand. With the converged ANN models derived by a training procedure, the temperature sensitivity of the substation load demand is easily obtained by the recall process. It is suggested that the substation load forecasting can be performed efficiently by the proposed method to support distribution operation effectively.