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Short-Term Load Forecasting by Artificial Intelligent Technologies
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Energies2018,11, 3283 hybridmodelwehaveconstructed. Table5 is thepredictionresult composedofMLP,andMAPEis usedasameasureofpredictionaccuracyandthepredictedresultswith thebestaccuracyaremarked inbold.Asshowninthe table,overall, amodelconsistingofnineandninenodes ineachhidden layer showedthebestperformance.Althoughthenineandsixnodes ineachhiddenlayershowedabetter performance inClusterA, themodelconsistingofnineandninenodeswasselectedtogeneralize the predictivemodel. (a) Cluster A (b) Cluster B (c) Cluster C Figure5.Resultsof similar timeseriesclassificationsusingdecisiontrees. Table5.MAPEresultsof themultilayerperceptron. Cluster# NumberofNeuronsinEachLayer 9-6-6-1 9-9-6-1 9-9-9-1 ClusterA 3.856 3.767 3.936 ClusterB 4.869 5.076 4.424 ClusterC 3.366 3.390 3.205 130
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Short-Term Load Forecasting by Artificial Intelligent Technologies
Titel
Short-Term Load Forecasting by Artificial Intelligent Technologies
Autoren
Wei-Chiang Hong
Ming-Wei Li
Guo-Feng Fan
Herausgeber
MDPI
Ort
Basel
Datum
2019
Sprache
englisch
Lizenz
CC BY 4.0
ISBN
978-3-03897-583-0
Abmessungen
17.0 x 24.4 cm
Seiten
448
Schlagwörter
Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
Kategorie
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Austria-Forum
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Short-Term Load Forecasting by Artificial Intelligent Technologies