TY - JOUR
T1 - Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting
AU - An, Ning
AU - Zhao, Weigang
AU - Wang, Jianzhou
AU - Shang, Duo
AU - Zhao, Erdong
PY - 2013/1/1
Y1 - 2013/1/1
N2 - 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.
AB - 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.
KW - EMD-based signal filtering
KW - Electricity demand forecasting
KW - Feedforward neural network
KW - Multi-output forecasting
KW - Seasonal adjustment
UR - http://www.scopus.com/inward/record.url?scp=84871717701&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2012.10.035
DO - 10.1016/j.energy.2012.10.035
M3 - Article
AN - SCOPUS:84871717701
SN - 0360-5442
VL - 49
SP - 279
EP - 288
JO - Energy
JF - Energy
IS - 1
ER -