Hybrid Local General Regression Neural Network and Harmony Search Algorithm for Electricity Price Forecasting

Abstract

Proposing a new precise price forecasting method is still a challenging task as electricity price signals generally exhibit various complex features. In this paper, a new approach for electricity price forecasting called hybrid local general regression neural network, and harmony search algorithm (LGRNN-HSA) is proposed. The proposed LGRNN-HSA is developed by combining the coordinate delay (CD) method, local forecasting paradigm, general regression neural network (GRNN) and harmony search algorithm (HSA). The CD is employed in the proposed method to reconstruct the time series dataset. Then the local forecasting paradigm is utilized with GRNN to predict the future price based on the nearest neighbours only so that the shortcomings in global forecasting methods can be overcome. To enhance the forecasting accuracy, the HSA is employed to optimize not only the parameters of local forecasting paradigm but also the smooth parameter which has a great effect on the accuracy of GRNN. In LGRNN-HSA, a different smooth parameter for every training point is utilized instead of utilizing a constant value in GRNN. To verify the forecasting accuracy of the LGRNN-HSA, a real-world price and demand dataset is employed. The simulation results prove that the LGRNN-HSA significantly improved forecasting accuracy compared to other methods.

Keywords

• Electricity price forecasting
• local forecasting
• harmony search algorithm
• general regression neural network
• parameters optimization