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Energies2018,11, 1138
Figure3. Primaryelectrical systeminWanjiangareaabove110kv, includingelectricpowerplants,
transmission buses, converting stations, and user loads. The feeders are marked under their
corresponding loadsides.
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Figure4.LoadcurveofCustomer53990001fromFeeder2duringamonth,wherethesamplingperiodis
15min.
Besides thehistorical loadcurves, short-termloadforecasting is influencedbythe factorsofdate,
weatherandtemperature. Therealhistoricaldataofweatherandtemperature in thecorresponding
area inDongguanCitywereobtainedonline fromtheweather forecastwebsites. Thecategoriesof
weather includesunny,cloud,overcast, light rain, shower,heavyrain, typhoonandsnow.Thedate
featurescanbefoundincalendars.
2.3. ClusteringandQuantization
Thecustomofelectricityuseandcharacteristicsof loadcurvearedifferentamongthedifferent
categoriesofcustomerssuchas industrial customers, residential customersandinstitutioncustomers.
Thedifferentcharacteristicswouldaffect theperformanceof forecasting. Trainingforecastingnetworks
with each customer separatelywouldbe ahuge computation and storageproblem. Therefore, in
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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