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Energies2018,11, 3283
(a) First quarter (1 January–31 March) in 2017
(b) Second quarter (1 April–30 June) in 2017
(c) Third quarter (1 July–30 September) in 2017
(d) Fourth quarter (1 October–31 December) in 2017
Figure8.Dailyelectrical loadforecastingforuniversitycampus.
6.Conclusions
In thispaper,weproposedahybridmodel forshort-termloadforecastingforhighereducational
institutions, suchasuniversities,usingrandomforest andmultilayerperceptron. Toconstructour
forecastmodel,wefirstgroupeduniversitybuildings intoanacademiccluster, science/engineering
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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