Electricity price forecasting using a new data fusion algorithm

• Published in: IET generation, transmission & distribution Vol. 9; no. 12; pp. 1382 - 1390
• Main Authors: Darudi, Ali, Bashari, Masoud, Javidi, Mohammad Hossein
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 04.09.2015
• Subjects: adaptive neuro‐fuzzy inference system; Algorithms; artificial neural network; autoregressive moving average; autoregressive moving average processes; Data fusion; data fusion algorithm; Data integration; electricity price forecasting; Electricity pricing; Engines; Forecasting; fuzzy neural nets; hybrid price forecasting framework; inference mechanisms; Markets; modified ordered weighted average; modified OWA; power engineering computing; power markets; Pricing; primary forecast engines; sensor fusion; Spanish electricity market; artificial neural network; electricity price forecasting; autoregressive moving average processes; fuzzy neural nets; sensor fusion; inference mechanisms; power engineering computing; modified ordered weighted average; data fusion algorithm; primary forecast engines; autoregressive moving average; hybrid price forecasting framework; Spanish electricity market; adaptive neuro-fuzzy inference system; power markets; modified OWA; pricing
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2014.0653

Accurate price forecasting is crucial for all market participants in electricity markets. This study presents a hybrid price forecasting framework based on a new data fusion algorithm. Owing to the complexity and distinct nature of the electricity price, a single forecast engine cannot capture all different patterns of the price signals. Hence, this study focuses on a hybrid forecasting method to extract the advantages of several forecasting engines. In the proposed method, artificial neural network, adaptive neuro-fuzzy inference system and autoregressive moving average are employed as primary forecast engines (agents) which provide three independent price forecasts. Then, a new data fusion algorithm, the modified ordered weighted average (modified OWA), is proposed to combine the three forecasts to generate a single unified price forecast. Hopefully, the fusion's output outperforms all the agents’ forecasts. The author's proposed fusion algorithm, unlike conventional OWA, uses the feedback from the agents’ error. The proposed framework is evaluated on the Spanish electricity market. The results confirm the ability of the proposed fusion framework to provide more accurate forecasts compared with the input agents forecasts. Results are also compared with some of the recent electricity price forecasting methods.