Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models

Abstract Short term load forecasting (STLF) is an integral part of power system operations as it is essential for ensuring supply of electrical energy with minimum expenses. This paper proposes a hybrid method based on wavelet transform, Triple Exponential Smoothing (TES) model and weighted nearest neighbor (WNN) model for STLF. The original demand series is decomposed, thresholded and reconstructed into deterministic and fluctuation series using Haar wavelet filters. The deterministic series that reflects the slow dynamics of load data is modeled using TES model while the fluctuation series that reflects the faster dynamics is fitted by WNN model. The forecasts of two subseries are composed to obtain the 24 h ahead load forecast. The performance of the proposed model is evaluated by applying it to forecast the day ahead load in the electricity markets of California and Spain. The results obtained demonstrate the forecast accuracy of the proposed technique.

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