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Energies2018,11, 3283
to thenewHVACsystem. Lastly,ClusterAshowedahighforecastingerroron29November2017.
It turnedout thatat that time, therewereseveralmissingvalues in theactualpowerconsumption.
Thiskindofproblemcanbedetectedbyusingtheoutlierdetectiontechnique.
Table11.MAEcomparisonforeachforecastingmodel.
ForecastingModel Cluster#
ClusterA ClusterB ClusterC
MR 4155.572 4888.821 1262.985
DT 3897.741 5054.069 1708.709
GBM 2764.128 3916.945 1122.530
SVR 2236.318 3956.907 898.963
SNN 2319.696 3469.775 919.014
MLP 2255.537 2795.246 910.351
RF 2708.848 3235.855 1063.731
RF+MLP 2208.072 2742.543 860.989
(a) Cluster A
(b) Cluster B
(c) Cluster C
Figure7.DistributionofeachmodelbyMAPE.
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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