A novel hybrid approach using wavelet, firefly algorithm, and fuzzy ARTMAP for day-ahead electricity price forecasting

Abstract

This paper presents a novel hybrid intelligent algorithm utilizing a data filtering technique based on wavelet transform (WT), an optimization technique based on firefly (FF) algorithm, and a soft computing model based on fuzzy ARTMAP (FA) network in order to forecast day-ahead electricity prices in the Ontario market. A comprehensive comparative analysis with other soft computing and hybrid models shows a significant improvement in forecast error by more than 40% for daily and weekly price forecasts, through the application of a proposed hybrid <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm WT}+{\rm FF}+{\rm FA}$</tex></formula> model. Furthermore, low values obtained for the forecast mean square error (FMSE) and mean absolute error (MAE) indicate high degree of accuracy of the proposed model. Robustness of the proposed hybrid intelligent model is measured by using the statistical index (error variance). In addition, the good forecast performance and the rapid adaptability of the proposed hybrid <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm WT}+{\rm FF}+{\rm FA}$</tex></formula> model are also evaluated using the PJM market data.