Application of echo state networks in short-term electric load forecasting

The paper presents the application of echo state network (ESN) to short-term load forecasting (STLF) problem in power systems for both 1-h and 24-h ahead predictions while using the least number of inputs: current-hour load, predicted target-hour temperature, and only for 24-h ahead forecasting, day-type index. The study is much attractive due to inclusion of weekends/holidays what makes STLF problem much more difficult. The main aim is to show the great capabilities of ESN as a stand-alone forecaster to learn complex dynamics of hourly electric load time series and forecast the near future loads with high accuracies. ESN as the state-of-the-art recurrent neural network (RNN) gains a reservoir of dynamics tapped by trained output units with a simple and fast single-stage training process. Furthermore, the application of ESN to predict the target-hour temperature needed by ESN-based load forecasters is examined. Since temperature prediction errors affect load forecasting accuracy, effects of such errors on ESN-based load forecasting are studied by both sensitivity analysis and applying noisy temperature series. Real hourly load and temperature data of a North-American electric utility is used as the data set. The results reflect that the ESN-based STLF method provides load forecasts with acceptable high accuracy.

► We apply ESN to 1-h and 24-h ahead load and temperature forecasting.
► The aim is to reveal the great capabilities of ESN as a stand-alone load forecaster.
► ESN inputs are kept to a minimum and weekends/holidays data are included.
► Sensitivity analysis of load forecast error to temperature prediction errors is done.
► Results are acceptably accurate for a long forecasting horizon with one time training.