Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. (15th March 2017)

• Record Type: Journal Article
• Authors: Wang, Deyun; Luo, Hongyuan; Grunder, Olivier; Lin, Yanbing; Guo, Haixiang
• Is Part Of: Applied energy. Volume 190(2017)
• Page Start: 390
• Page End: 407
• Publication Date: 2017-03-15
• Subjects: Electricity price forecasting -- Multi-step ahead -- Fast ensemble empirical mode decomposition -- Variational mode decomposition -- Firefly algorithm -- Back propagation neural network; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2016.12.134
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A hybrid two-layer decomposition-ensemble model is proposed for multi-step ahead electricity price forecasting. VMD is specifically applied to further decompose the high frequency IMFs generated by FEEMD. The BP model optimized by FA obtains better forecasting performance. The proposed model is tested using the real-world data of Australian and French electricity markets. Abstract: In the deregulated competitive electricity market, the price which reflects the relationship between electricity supply and demand is one of the most important elements, making it crucial for all market participants to precisely forecast the electricity price. However, electricity price series usually has complex features such as non-linearity, non-stationarity and volatility, which makes the price forecasting turn out to be very difficult. In order to improve the accuracy of electricity price forecasting, this paper first proposes a two-layer decomposition technique and then develops a hybrid model based on fast ensemble empirical mode decomposition (FEEMD), variational mode decomposition (VMD) and back propagation (BP) neural network optimized by firefly algorithm (FA). The proposed model is unique in the sense that VMD is specifically applied to further decompose the high frequency intrinsic mode functions (IMFs) generated by FEEMD into a number of modes in order to improve the forecast accuracy. To validate the effectiveness and accuracy of the proposed model, three electricity price series respectively collected from the real-world electricity markets of Australia and France are adopted to conduct the empirical study. The results indicate that the proposed model outperforms the other considered models over horizons of one-step, two-step, four-step and six-step ahead forecasting, which shows that the proposed model has superior performances for both one-step and multi-step ahead forecasting of electricity price.