A hybrid Elman recurrent neural network, group search optimization, and refined VMD-based framework for multi-step ahead electricity price forecasting

Abstract

This paper presents a synergy of three methods for training the Elman recurrent neural network to forecast the multi-step-ahead electricity price in an electric power system. Electricity prices are characterized as non-stationary time series data that entail vigorous learning model for predicting the future electricity price from past data. To accomplish this, an enhanced hybrid framework that integrates the refined variational mode decomposition method and the group search optimization algorithm is proposed for training the Elman recurrent neural network. The variational mode decomposition method is optimized using a complement particle swarm optimization method so as to decompose the non-stationary pricing data into optimum number of intrinsic mode functions. Subsequently, based on the power values of intrinsic mode, functions are further filtered and used as the input data to train the Elman neural network. The group search optimization algorithm is used to optimize the weights of the Elman neural network. Three real-time time series non-stationary data for multi-step ahead price prediction are adopted from Australian, British, and Indian power markets and experimented using the proposed forecasting model.