Short term electric load forecasting using a neural network with fuzzy hidden neurons

Short term electric load forecasting with a neural network based on fuzzy rules is presented. In this network, fuzzy membership functions are represented using combinations of two sigmoid functions. A new scheme for augmenting the rule base is proposed. The network employs outdoor temperature forecast as one of the input quantities. The influence of imprecision in this quantity is investigated. The model is shown to be capable of also making reasonable forecasts in exceptional weekdays. Forecasting simulations were made with three different time series of electric load. In addition, the neuro-fuzzy method was tested at two electricity works, where it was used to produce forecasts with 1–24 hour lead times. The results of these one month real world tests are represented. Comparative forecasts were also made with the conventional Holt-Winters exponential smoothing method. The main result of the study is that the neuro-fuzzy method requires stationarity from the time series with respect to training data in order to give clearly better forecasts than the Holt-Winters method.