Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and randomvectorfunctionallink networ...
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ISBN:
(纸本)9781509018970
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and randomvectorfunctionallink network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.
As the production quality index of grinding processes,particle size(PS) is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel random vector functional link networks(RVFLN),namely,rob...
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As the production quality index of grinding processes,particle size(PS) is hard to be measured in real *** achieve the PS estimation,this paper proposes a novel random vector functional link networks(RVFLN),namely,robust regularized *** incorporating the weighted least squares(WLS) and regularization techniques into the original RVFLN and further adopting a nonparametric kernel density estimation(NKDE) method to choose the weighted term,the generalization and robustness of network have been *** order to ensure the quality and computational load of network in online application,the different online learning versions are presented according to the various time scales of data sampling,*** experimental studies are first carried out based on the UCI and Statlib standard data *** last,the actual industrial grinding operation data are used to verify the effectiveness of the proposed method in term of PS estimation.
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and randomvectorfunctionallink networ...
详细信息
ISBN:
(纸本)9781509018987
Short-term electricity load demand forecasting is a critical process in the management of modern power system. An ensemble method composed of Empirical Mode Decomposition (EMD) and randomvectorfunctionallink network (RVFL) is presented in this paper. Due to the randomly generated weights between input and hidden layers and the close form solution for parameter tuning, RVFL network is a universal approximator with the advantages of fast training. By introducing ensemble approach via EMD into RVFL network, the performance can be significantly improved. Five electricity load demand datasets from Australian Energy Market Operator (AEMO) were used to evaluate the performance of the proposed method. The attractiveness of the proposed EMD based RVFL network can be demonstrated by the comparison with six benchmark methods.
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