User-Based Load Visualization of Categorical Forecasted Smart Meter Data Using LSTM Network

Electricalloadforecastingisanessentialfeatureinpowersystemsplanning,operationandcontrol.The non-linearityandnon-stationarynatureofthedata,however,posesachallengeintermsofaccuracy. Thisarticleexploresadeeplearningtechnique,alongshort-termmemoryrecurrentneuralnetworkbasedframeworktotacklethistrickyissue.Theproposedmachinelearningmodelframeworkistested onrealtimeresidentialsmartmeterdatashowingpromisingresults.Awebapplicationhasalsobeen developedtoallowconsumerstohaveaccesstogreaterlevelsofinformationandfacilitatedecisionmakingat theirend.Theperformanceof theproposedmodel isalsocomprehensivelycompared toothermethodsinthefieldofloadforecastingshowingmoreaccurateresultsforthefunctionof forecastingofloadonshorttermbasis. KEywoRDS Load Forecasting, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Residential Load, Smart Grid, Smart Meter, Support Vector Machine (SVM)

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