Autors: Pavlatos, C., Makris, E., Fotis, G., Vita, V., Mladenov, V. M.
Title: Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network
Keywords: forecastingelectricitydemand;bidirectionalLSTM;short-termpre

Abstract: Preciseanticipationofelectricaldemandholdscrucialimportancefortheoptimaloperation ofpowersystemsandtheeffectivemanagementofenergymarketswithinthedomainofenergy planning. Thisstudybuildsonpreviousresearchfocusedontheapplicationofartificialneural networkstoachieveaccurateelectricalloadforecasting. Inthispaper,animprovedmethodology isintroduced,centeringaroundbidirectionalLongShort-TermMemory(LSTM)neuralnetworks (NN).TheprimaryaimoftheproposedbidirectionalLSTMnetworkistoenhancepredictiveperformancebycapturingintricatetemporalpatternsandinterdependencieswithintimeseriesdata. Whileconventionalfeed-forwardneuralnetworksaresuitableforstandalonedatapoints,energy consumptiondataarecharacterizedbysequentialdependencies,necessitatingtheincorporation ofmemory-basedconcepts. ThebidirectionalLSTMmodel isdesignedtofurnishtheprediction frameworkwiththecapacitytoassimilateandleverageinformationfrombothprecedingandforthcomingtimesteps.Thisaugmentationsignificantlybolsterspredictivecapabilitiesbyencapsu

References

    Issue

    MDPI Electronics, vol. 12, issue 22, pp. 1-13, 2023, Switzerland, MDPI, https://doi.org/10.3390/electronics12224652

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