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A hybrid approach to very small scale electrical demand forecasting

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Show simple item record Marinescu, Andrei Harris, Colin Dusparic, Ivana Cahill, Vinny Clarke, Siobhán 2014-06-24T10:36:45Z 2014-06-24T10:36:45Z 2014
dc.description peer-reviewed en_US
dc.description.abstract Microgrid management and scheduling can considerably benefit from day-ahead demand forecasting. Until now, most of the research in the field of electrical demand forecasting has been done on large-scale systems, such as national or municipal level grids. This paper examines a hybrid method that attempts to accurately estimate day-ahead electrical demand of a small community of houses resembling the load of a single transformer, the equivalent sizing of a small virtual power plant or microgrid. We have combined the advantages of several forecasting methods into a novel hybrid approach: artificial neural networks, fuzzy logic, auto-regressive moving average and wavelet smoothing. The combined system has been tested over two different scenarios, comprising communities of 90 houses and 230 houses, sampled from a smart-meter field trial in Ireland. Our hybrid approach achieves results of 3.22% NRMSE and 2.39% NRMSE respectively, leading to general improvements of 11%- 28% when compared to the individual methods. en_US
dc.language.iso eng en_US
dc.publisher IEEE Computer Society en_US
dc.relation.ispartofseries IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT);pp. 1-5
dc.rights “© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.” en_US
dc.subject demand forecasting en_US
dc.subject hybrid en_US
dc.subject microgrid en_US
dc.subject VPP en_US
dc.title A hybrid approach to very small scale electrical demand forecasting en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.contributor.sponsor SFI en_US
dc.relation.projectid 10/CE/I1855 en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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