| 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 References Issue
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