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dc.contributor.authorChen, Yi-Tingen_US
dc.contributor.authorSun, Edward W.en_US
dc.contributor.authorLin, Yi-Bingen_US
dc.date.accessioned2020-10-05T02:01:06Z-
dc.date.available2020-10-05T02:01:06Z-
dc.date.issued2020-08-01en_US
dc.identifier.issn0927-7099en_US
dc.identifier.urihttp://dx.doi.org/10.1007/s10614-019-09960-5en_US
dc.identifier.urihttp://hdl.handle.net/11536/155140-
dc.description.abstractTraditional methods applied in electricity demand forecasting have been challenged by the course of dimensionality arisen with a growing number of distributed or decentralized energy systems are employing. Without manually operated data preprocessing, classic models are not well-calibrated for their robustness when dealing with the disruptive elements (e.g., demand changes in holidays and extreme weather). Based on the application of big data driven analytics, we propose a novel machine learning method originating from the parallel neural networks for robust monitoring and forecasting power demand to enhance supervisory control and data acquisition for new industrial tendency such as Industry 4.0 and Energy IoT. Through our approach, we generalize the implementation of machine learning by using classic feed-forward neural networks, for parallelization in order to let the proposed method achieve superior performance when dealing with high dimensionality and disruptiveness. With the high-frequency data of consumption in Australia from January 2009 to December 2015, the overall empirical results confirm that our proposed method performs significantly better for dynamic monitoring and forecasting of power demand comparing with the classic methods.en_US
dc.language.isoen_USen_US
dc.subjectBig dataen_US
dc.subjectEnergyen_US
dc.subjectForecastingen_US
dc.subjectMachine learningen_US
dc.subjectNeural networks (PNNs)en_US
dc.titleMachine learning with parallel neural networks for analyzing and forecasting electricity demanden_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s10614-019-09960-5en_US
dc.identifier.journalCOMPUTATIONAL ECONOMICSen_US
dc.citation.volume56en_US
dc.citation.issue2en_US
dc.citation.spage569en_US
dc.citation.epage597en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000550297300012en_US
dc.citation.woscount0en_US
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