Autors: Pavlatos, C., Makris, E., Fotis, G., Vita, V., Mladenov, V. M.
Title: Utilization of Artificial Neural Networks for Precise Electrical Load Prediction
Keywords: :electricalload;recurrentneuralnetwork;short-termforecasting

Abstract: :Intheenergy-planningsector,theprecisepredictionofelectricalloadisacriticalmatter forthefunctionaloperationofpowersystemsandtheefficientmanagementofmarkets.Numerous forecastingplatformshavebeenproposedintheliteraturetotacklethisissue.Thispaperintroduces aneffectiveframework,codedinPython,thatcanforecastfutureelectricalloadbasedonhourly ordailyloadinputs.Theframeworkutilizesarecurrentneuralnetworkmodel,consistingoftwo simpleRNNlayersandadenselayer,andadoptstheAdamoptimizerandtanhlossfunctionduring thetrainingprocess.Dependingonthesizeoftheinputdataset,theproposedsystemcanhandle bothshort-termandmedium-termload-forecastingcategories.Thenetworkwasextensivelytested usingmultipledatasets,andtheresultswerefoundtobehighlypromising.Allvariationsofthe networkwereabletocapturetheunderlyingpatternsandachievedasmalltesterrorintermsof rootmeansquareerrorandmeanabsoluteerror.Notably,theproposedframeworkoutperformed morecomplexneuralnetworks,witharootmeansquareerrorof0.033,indicatingahighdegreeof ac

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    Вид: статия в списание, публикация в издание с импакт фактор, публикация в реферирано издание, индексирана в Scopus и Web of Science