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Energies2018,11, 1138
networkscanbe trainedwithsamples in thesamecategoryaccordingto thecustomer tobepredicted,
so that the interferenceofelectricityusecharacteristicscanbereduced.
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Figure7.Loadcurvesof30customers inCategory2.
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Figure8.Loadcurvesof30customers inCategory3.
3.2. TheDetailedNetworkStructureandParameters
ThedetailedstructureofwholenetworkareshowninTable5. Theparametersof thenetwork
aresetas showninTable6. Thestructureandparametersare set forbetterperformanceaccording
to themultipleexperiments forcustomers inWanjiangarea. TheāRMSpropāoptimizer ischosenfor
itsbetterperformance inrecurrentneuralnetworks. Theparameterscanbeadjustedfor thedifferent
practicalsituations. Inthispaper, thenumberofepochisset to200fortheproposedmethodandcanbe
adjustedfor thecomparedmethods. The training is stoppedwhentheerrordecreases toasteadystate.
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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