Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting

Ning An, Weigang Zhao, Jianzhou Wang, Duo Shang and Erdong Zhao

Energy, 2013, vol. 49, issue C, 279-288

Abstract: For accurate electricity demand forecasting, this paper proposes a novel approach, MFES, that combines a multi-output FFNN (feedforward neural network) with EMD (empirical mode decomposition)-based signal filtering and seasonal adjustment. In electricity demand forecasting, noise signals, caused by various unstable factors, often corrupt demand series. To reduce these noise signals, MFES first uses an EMD-based signal filtering method which is fully data-driven. Secondly, MFES removes the seasonal component from the denoised demand series and models the resultant series using FFNN model with a multi-output strategy. This multi-output strategy can overcome the limitations of common multi-step-ahead forecasting approaches, including error amplification and the neglect of dependency between inputs and outputs. At last, MFES obtains the final prediction by restoring the season indexes back to the FFNN forecasts. Using the half-hour electricity demand series of New South Wales in Australia, this paper demonstrates that the proposed MFES model improves the forecasting accuracy noticeably comparing with existing models.

Keywords: EMD-based signal filtering; Seasonal adjustment; Feedforward neural network; Electricity demand forecasting; Multi-output forecasting (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (56)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:49:y:2013:i:c:p:279-288

DOI: 10.1016/j.energy.2012.10.035

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