A wavelet-nearest neighbor model for short-term load forecasting

Load forecasts of short lead times ranging from an hour to a day ahead are essential for improving the economic efficiency and reliability of power systems. This paper proposes a hybrid model based on the wavelet transform (WT) and the weighted nearest neighbor (WNN) techniques to predict the day ahead electrical load. The WT is used to decompose the load series into deterministic series and fluctuation series that reflect the changing dynamics of data. The two subseries are then separately forecast using appropriately fitted WNN models. The final forecast is obtained by composing the predicted results of each subseries. The hourly electrical load of California and Spanish energy markets are taken as experimental data and the mean absolute percentage error (MAPE), Weekly MAPE (WMAPE) and Monthly MAPE (MMAPE) are computed to evaluate the forecasting performance of the next-day load forecasts. The forecasting efficiency of the proposed model is evaluated using db2, db4, db5 and bior 3.1 wavelets. The results demonstrate the forecasting accuracy of the proposed hybrid model.