TY - JOUR
T1 - Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine
AU - Rafiei Foroushani, Mehdi
AU - Niknam, Taher
AU - Aghaei, Jamshid
AU - Shafie-khah, Miadreza
AU - P.S.Catalão, João
PY - 2018/11
Y1 - 2018/11
N2 - Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine for training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method.
AB - Competitive transactions resulting from recent restructuring of the electricity market, have made achieving a precise and reliable load forecasting, especially probabilistic load forecasting, an important topic. Hence, this paper presents a novel hybrid method of probabilistic electricity load forecasting, including generalized extreme learning machine for training an improved wavelet neural network, wavelet preprocessing and bootstrapping. In the proposed method, the forecasting model and data noise uncertainties are taken into account while the output of the model is the load probabilistic interval. In order to validate the method, it is implemented on the Ontario and Australian electricity markets data. Also, in order to remove the influence of model parameters and data on performance validation, Friedman and post-hoc tests, which are non-parametric tests, are applied to the proposed method. The results demonstrate the high performance, accuracy, and reliability of the proposed method.
KW - Probabilistic forecasting
KW - bootstrapping
KW - generalized extreme learning machine
KW - improved wavelet neural network
KW - wavelet processing
UR - http://www.scopus.com/inward/record.url?scp=85042362642&partnerID=8YFLogxK
U2 - 10.1109/TSG.2018.2807845
DO - 10.1109/TSG.2018.2807845
M3 - Journal article
SN - 1949-3053
VL - 9
SP - 6961
EP - 6971
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 6
M1 - 8298533
ER -