Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/139607
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dc.contributor.authorQiu, Xuehengen_US
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen_US
dc.contributor.authorAmaratunga, Gehan A. J.en_US
dc.date.accessioned2020-05-20T08:02:12Z-
dc.date.available2020-05-20T08:02:12Z-
dc.date.issued2018-
dc.identifier.citationQiu, X., Suganthan, P. N., & Amaratunga, G. A. J. (2018). Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting. Knowledge-Based Systems, 145, 182-196. doi:10.1016/j.knosys.2018.01.015en_US
dc.identifier.issn0950-7051en_US
dc.identifier.urihttps://hdl.handle.net/10356/139607-
dc.description.abstractShort-term electric load forecasting plays an important role in the management of modern power systems. Improving the accuracy and efficiency of electric load forecasting can help power utilities design reasonable operational planning which will lead to the improvement of economic and social benefits of the systems. A hybrid incremental learning approach composed of Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Random Vector Functional Link network (RVFL) is presented in this work. RVFL network is a universal approximator with good efficiency because of the randomly generated weights between input and hidden layers and the close form solution for parameter computation. By introducing incremental learning, along with ensemble approach via DWT and EMD into RVFL network, the forecasting performance can be significantly improved with respect to both efficiency and accuracy. The electric load datasets from Australian Energy Market Operator (AEMO) were used to evaluate the effectiveness of the proposed incremental DWT-EMD based RVFL network. Moreover, the attractiveness of the proposed method can be demonstrated by the comparison with eight benchmark forecasting methods.en_US
dc.description.sponsorshipNRF (Natl Research Foundation, S’pore)en_US
dc.language.isoenen_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.rights© 2018 Elsevier B.V. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleEnsemble incremental learning Random Vector Functional Link network for short-term electric load forecastingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1016/j.knosys.2018.01.015-
dc.identifier.scopus2-s2.0-85042295727-
dc.identifier.volume145en_US
dc.identifier.spage182en_US
dc.identifier.epage196en_US
dc.subject.keywordsEmpirical Mode Decompositionen_US
dc.subject.keywordsDiscrete Wavelet Transformen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
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