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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
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Short-Term Load Forecasting by Artificial Intelligent Technologies
- Title
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Authors
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Editor
- MDPI
- Location
- Basel
- Date
- 2019
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Size
- 17.0 x 24.4 cm
- Pages
- 448
- Keywords
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Category
- Informatik