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Energies2018,11, 2080
4.Results
Theresultsexpressed in this sectioncorrespondto the forecastingperiodof2017. Eachsubsection
presents theaccuracyofboth techniques (ARandNN)whenthecorrespondentparameterorexternal
condition changes. In addition, these results have been analyzedunder the categories described
inSection2.3.
4.1.HistoricalLoadAvailability
The results shown inTable 2 represent the effect of increasing the number of previous years
considered in the trainingof themodel from3to7 forbothmodels. Theresults showagenerallymore
accurateperformancebytheARmodelespeciallywith feweryearsofdata (1.50%vs. 2.17%). TheNN
model,however,benefitsmore fromtheavailabilityofmoredataandthisdifference is reducedto0.1%
whensevenyearsareused. TheARmodel showsvery little improvement from3 to7yearswhile
theNNmodelappears tobeable tobenefit fromevenlonger trainingdataas itsperformanceonall
categoriescontinues to improvefrom5to7years (seeFigure8).Unfortunately, theavailabledatabase
isnotyetdeepenoughto test this.
Table2.Forecastingerror (RMSE)with trainingperiods from3to7years.
TypeofDay 3-Years 5-Years 7-Years
AR NN AR NN AR NN
Overall 1.50% 2.17% 1.52% 1.72% 1.45% 1.55%
Regular 1.44% 1.96% 1.47% 1.57% 1.40% 1.44%
Special 1.91% 3.62% 1.81% 2.71% 1.81% 2.31%
Hot 1.63% 2.65% 1.53% 2.08% 1.55% 1.93%
Cold 1.61% 2.79% 1.73% 1.81% 1.72% 1.48%
Testconditions: 10neurons (10N),10redundantnetworks (10RN),5 temperature locations (5TL),12monthtraining
freq(12MF),7 lags forARand14forNN(7/14LAG).
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3-yrs 5-yrs 7-yrs
Accuracy vs training period length
AR NN
Figure8.Overall forecastingerror (RMSE)with trainingperiods from3to7years.
Regardingthecategorizedresults, regulardaysobtainalmost thesameresultwhile inhotand
special days theARoutperforms theNNmodel. However, colddays are clearly forecastedmore
accuratelybytheNNmodel. Thiscouldimplythat the linearrestrictionpresent intheARmodel limits
its capacity tomodel thebehaviorof the loadwith thedata treatmentused.
149
Short-Term Load Forecasting by Artificial Intelligent Technologies
- Titel
- Short-Term Load Forecasting by Artificial Intelligent Technologies
- Autoren
- Wei-Chiang Hong
- Ming-Wei Li
- Guo-Feng Fan
- Herausgeber
- MDPI
- Ort
- Basel
- Datum
- 2019
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-03897-583-0
- Abmessungen
- 17.0 x 24.4 cm
- Seiten
- 448
- Schlagwörter
- Scheduling Problems in Logistics, Transport, Timetabling, Sports, Healthcare, Engineering, Energy Management
- Kategorie
- Informatik