TY - GEN
T1 - A localized NARX Neural Network model for Short-term load forecasting based upon Self-Organizing Mapping
AU - Li, Hanshen
AU - Zhu, Yuan
AU - Hu, Jinglu
AU - Li, Zhe
N1 - Funding Information:
This work was in part supported by Shanghai Jiao Tong University Research Support and Waseda University Research Support. The principal author of this paper, Hanshen Li would like to appreciate the chance for being a double master degree student between Waseda University and Shanghai Jiao Tong University. The author also acknowledges Chinese State Energy Smart Grid R&D Center (Shanghai) to provide support. Thanks to all the professors and buddies who provide valuable advice. This research is dedicated to his parents for their endless love, support, and encouragement.
Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/25
Y1 - 2017/7/25
N2 - As a routine within the planning and operation of the electrical power system, Short-term load forecasting (STLF) is an essential issue in energy fields. Its prediction accuracy and precision specifically have an effect on the basic safety, stability and economic efficiency of the power system. Moreover, the actual forecasting result also has an impact on operations such as startup and shutdown of the power units, power switching, equipment maintenance, etc. Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network has been utilized for STLF and proved its effectiveness. This paper proposes a localized Bayesian-Regularization NARX Neural Network model combined with Self-Organizing Mapping (SOM). SOM Neural Network is utilized to extract the meteorological distribution and K-means is utilized to cluster the data. Assessment results depending on half-hourly Australian Grid data and Meteorological data demonstrate that the enhanced model can provide a higher accuracy prediction, which could bring additional economic benefits and extensive social advantages.
AB - As a routine within the planning and operation of the electrical power system, Short-term load forecasting (STLF) is an essential issue in energy fields. Its prediction accuracy and precision specifically have an effect on the basic safety, stability and economic efficiency of the power system. Moreover, the actual forecasting result also has an impact on operations such as startup and shutdown of the power units, power switching, equipment maintenance, etc. Nonlinear Autoregressive models with Exogenous Input (NARX) Neural Network has been utilized for STLF and proved its effectiveness. This paper proposes a localized Bayesian-Regularization NARX Neural Network model combined with Self-Organizing Mapping (SOM). SOM Neural Network is utilized to extract the meteorological distribution and K-means is utilized to cluster the data. Assessment results depending on half-hourly Australian Grid data and Meteorological data demonstrate that the enhanced model can provide a higher accuracy prediction, which could bring additional economic benefits and extensive social advantages.
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U2 - 10.1109/IFEEC.2017.7992133
DO - 10.1109/IFEEC.2017.7992133
M3 - Conference contribution
AN - SCOPUS:85034055232
T3 - 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017
SP - 749
EP - 754
BT - 2017 IEEE 3rd International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd IEEE International Future Energy Electronics Conference and ECCE Asia, IFEEC - ECCE Asia 2017
Y2 - 3 June 2017 through 7 June 2017
ER -