Seite - 166 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2018,11, 2038 23dummies for thehourof theday, sixdummies for thedayof theweek,11dummies for themonth of theyear,fivedummies for specialdays (FH1, . . . , FH5), twopredictorsofhistoric temperatures (lags48hand72h),andsixpredictorsofhistoric loads (lags48h,72h,96h,120h,144h,and168h). Foreachensemblemethod, theparameterselectionhasbeendevelopedandmeasuresofvariable importancehavebeenobtained(seeTable3forthemeaningofeachterm). Inordertohavereproducible models and comparable results, the same seedwas selected in all procedures that require random sampling. In the caseofbaggingandrandomforest,wehaveselectedanoptimalnumberof trees (ntree) through theOOBerror estimate andwehaveordered thepredictors according to thenode impurity importancemeasure, see [28]. Forbagging, thenumberofpredictors thatareconsideredat eachsplitmustbe the totalnumberofpredictors,whereas in thecaseof randomforest, theoptimal parameterhasbeenselectedusingtheOOBerrorestimate fordifferentvaluesofmtry. In thecaseof conditional forest, theconditionalvariable importancemeasure introducedin[40]hasbeenconsidered, whichbetter reflects the true impactofeachpredictor inpresenceofcorrelatedpredictors. While inbaggingandrandomforest theOOBerrorwasusedto tunetheparameters, in thecase ofconditional forestandXGBoost theparameterswere tunedbymeansofcrossvalidationwithfive folds (approximately oneyear in each fold). As for conditional forest, only twoparameters need tobe tuned(ntreeandmtry), but inXGBoost, therearemoreparameters to tune.Althoughonecan applycrossvalidationtaking intoaccountamulti-dimensiongridwithallof theparameters to tune (thisapproachwould implyahighcomputationalcost),weconsideredasimplificationof thesearch selecting subsample=0.5,maxdepth=6 (appropriate inmost problems) and looking for a good combinationof“eta”and“nrounds”, seeTable4. The restofparametersof themethodweresetup bydefault, according to theRpackage [38]. In thecaseofXGBoost, featureshavebeenorderedby decreasing importancewhileusingthegainmeasuredefinedin[36]. Table3.Notation. Term Description ntree (N) Numberof treesor iterations inbagging, randomforestandconditional forest mtry Numberofpredictorsconsideredateachsplit inbagging, randomforestandconditional forest node impurity Importancemeasure inrandomforest max_depth Maximumdepthofa tree subsample Subsampleratioof the training instance eta Shrinkageor learningrate nrounds Numberofboosting iterations gain Fractionalcontributionofeachfeature to themodel Table4showstheresultsof theparameterselectionfor theXGBoostmethod.Recall thata lower learning rate eta implies a greater number of iterationsnround, but a too largenround can lead to overfitting. Combination (eta = 0.02, nrounds = 3400) provided the lowestRMSE and the highest R-squaredscores for the testdata,whereas (eta=0.01,nrounds=5700)got the lowestMAPE.However, anypairofparameters inTable4couldbeappropriatebecause they leadsimilaraccuracy. 166