Matches in SemOpenAlex for { <https://semopenalex.org/work/W2743882817> ?p ?o ?g. }
- W2743882817 endingPage "1186" @default.
- W2743882817 startingPage "1186" @default.
- W2743882817 abstract "The experience with deregulated electricity market has shown the increasingly important role of short-term electric load forecasting in the energy producing and scheduling. However, because of nonlinear, stochastic and nonstable characteristics associated with the electric load series, it is extremely difficult to precisely forecast the electric load. This paper aims to establish a novel ensemble model based on variational mode decomposition (VMD) and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm for multi-step ahead electric load forecasting. The proposed model is novel in the sense that VMD is firstly applied to decompose the original electric load series into a set of components with different frequencies in order to effectively eliminate the stochastic fluctuation characteristic so as to improve the overall prediction accuracy. The proposed ensemble model is tested using two electric load series collected from New South Wales (NSW) and Queensland (QLD) in the Australian electricity market. The experimental results show that: (1) the data preprocessing by VMD can effectively decrease the stochastic fluctuation characteristics that existed in the electric load series, consequently improving the whole forecasting accuracy, and; (2) the proposed forecasting model performs better than all other benchmark models for both one-step and multi-step ahead electric load forecasting." @default.
- W2743882817 created "2017-08-17" @default.
- W2743882817 creator A5009509812 @default.
- W2743882817 creator A5044905434 @default.
- W2743882817 creator A5076151134 @default.
- W2743882817 creator A5076674902 @default.
- W2743882817 creator A5089273861 @default.
- W2743882817 date "2017-08-11" @default.
- W2743882817 modified "2023-10-18" @default.
- W2743882817 title "An Ensemble Model Based on Machine Learning Methods and Data Preprocessing for Short-Term Electric Load Forecasting" @default.
- W2743882817 cites W1623239345 @default.
- W2743882817 cites W1871376293 @default.
- W2743882817 cites W1965596135 @default.
- W2743882817 cites W1970898534 @default.
- W2743882817 cites W1972581513 @default.
- W2743882817 cites W1976944686 @default.
- W2743882817 cites W1985594416 @default.
- W2743882817 cites W1995140642 @default.
- W2743882817 cites W2000982976 @default.
- W2743882817 cites W2001459659 @default.
- W2743882817 cites W2007221293 @default.
- W2743882817 cites W2010680143 @default.
- W2743882817 cites W2014928429 @default.
- W2743882817 cites W2022825719 @default.
- W2743882817 cites W2022912953 @default.
- W2743882817 cites W2023281112 @default.
- W2743882817 cites W2024692966 @default.
- W2743882817 cites W2055221518 @default.
- W2743882817 cites W2073002835 @default.
- W2743882817 cites W2089217930 @default.
- W2743882817 cites W2094306384 @default.
- W2743882817 cites W2095471434 @default.
- W2743882817 cites W2127842029 @default.
- W2743882817 cites W2133752269 @default.
- W2743882817 cites W2140208140 @default.
- W2743882817 cites W2285826949 @default.
- W2743882817 cites W2294812789 @default.
- W2743882817 cites W2329476579 @default.
- W2743882817 cites W2340247464 @default.
- W2743882817 cites W2345862676 @default.
- W2743882817 cites W2346227481 @default.
- W2743882817 cites W2571217044 @default.
- W2743882817 doi "https://doi.org/10.3390/en10081186" @default.
- W2743882817 hasPublicationYear "2017" @default.
- W2743882817 type Work @default.
- W2743882817 sameAs 2743882817 @default.
- W2743882817 citedByCount "39" @default.
- W2743882817 countsByYear W27438828172018 @default.
- W2743882817 countsByYear W27438828172019 @default.
- W2743882817 countsByYear W27438828172020 @default.
- W2743882817 countsByYear W27438828172021 @default.
- W2743882817 countsByYear W27438828172022 @default.
- W2743882817 countsByYear W27438828172023 @default.
- W2743882817 crossrefType "journal-article" @default.
- W2743882817 hasAuthorship W2743882817A5009509812 @default.
- W2743882817 hasAuthorship W2743882817A5044905434 @default.
- W2743882817 hasAuthorship W2743882817A5076151134 @default.
- W2743882817 hasAuthorship W2743882817A5076674902 @default.
- W2743882817 hasAuthorship W2743882817A5089273861 @default.
- W2743882817 hasBestOaLocation W27438828171 @default.
- W2743882817 hasConcept C11413529 @default.
- W2743882817 hasConcept C119599485 @default.
- W2743882817 hasConcept C119857082 @default.
- W2743882817 hasConcept C121332964 @default.
- W2743882817 hasConcept C127413603 @default.
- W2743882817 hasConcept C13280743 @default.
- W2743882817 hasConcept C143724316 @default.
- W2743882817 hasConcept C146733006 @default.
- W2743882817 hasConcept C151406439 @default.
- W2743882817 hasConcept C151730666 @default.
- W2743882817 hasConcept C154945302 @default.
- W2743882817 hasConcept C158622935 @default.
- W2743882817 hasConcept C165801399 @default.
- W2743882817 hasConcept C185798385 @default.
- W2743882817 hasConcept C205649164 @default.
- W2743882817 hasConcept C206658404 @default.
- W2743882817 hasConcept C2780150128 @default.
- W2743882817 hasConcept C41008148 @default.
- W2743882817 hasConcept C50644808 @default.
- W2743882817 hasConcept C61797465 @default.
- W2743882817 hasConcept C62520636 @default.
- W2743882817 hasConcept C77715397 @default.
- W2743882817 hasConcept C86803240 @default.
- W2743882817 hasConceptScore W2743882817C11413529 @default.
- W2743882817 hasConceptScore W2743882817C119599485 @default.
- W2743882817 hasConceptScore W2743882817C119857082 @default.
- W2743882817 hasConceptScore W2743882817C121332964 @default.
- W2743882817 hasConceptScore W2743882817C127413603 @default.
- W2743882817 hasConceptScore W2743882817C13280743 @default.
- W2743882817 hasConceptScore W2743882817C143724316 @default.
- W2743882817 hasConceptScore W2743882817C146733006 @default.
- W2743882817 hasConceptScore W2743882817C151406439 @default.
- W2743882817 hasConceptScore W2743882817C151730666 @default.
- W2743882817 hasConceptScore W2743882817C154945302 @default.
- W2743882817 hasConceptScore W2743882817C158622935 @default.
- W2743882817 hasConceptScore W2743882817C165801399 @default.
- W2743882817 hasConceptScore W2743882817C185798385 @default.
- W2743882817 hasConceptScore W2743882817C205649164 @default.