Page - 426 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies 2018,11, 213 Inorder toevaluate theperformanceof forecastingmodelsmoreaccurately, theMeanAbsolute PercentageError (MAPE) andCumulativeVariationofRootMeanSquareError (CV-RMSE)were employed. TheMAPEandCV-RMSEaredefinedbyEquations (7) and (8), respectively,whereyn denotes themeasuredvalue, yˆn is theestimatedvalue,andNrepresents thesamplesize. MAPE= 1 N N ∑ n=1 ∣∣∣∣yn− yˆnyn ∣∣∣∣ (7) CV−RMSE= √ 1 N N ∑ n=1 ( yn−yˆn yn )2 1 N N ∑ n=1 yn (8) Thedetailed experimental results are presentednumerically inTables 1 and 2. As shown in Tables1and2, theMAPEandCV-RMSEof theDeepEnergymodelare thesmallestandthegoodness oferror is thebestamongallmodels,namely,averageMAPEandCV-RMSEare9.77%and11.65%, respectively. TheMAPEofMLPmodel is the largest amongall of themodels; anaverage error is about15.47%.Ontheotherhand, theCV-RMSEofSVMmodel is the largestamongallmodels; an averageerror isabout17.47%.Accordingto theaverageMAPEandCV-RMSEvalues, theelectric load forecastingaccuracyof testedmodels indescendingorder isas follows:DeepEnergy,RF,LSTM,DT, SVM,andMLP. Table 1. The experimental results in terms of Mean Absolute Percentage Error (MAPE) given inpercentages. Test SVM RF DT MLP LSTM DeepEnergy #1 7.327408 7.639133 8.46043 9.164315 10.40804813 7.226127 #2 7.550818 8.196129 10.23476 11.14954 9.970662683 8.244051 #3 13.07929 10.11102 12.14039 19.99848 14.85568499 11.00656 #4 16.15765 17.27957 19.86511 22.45493 12.83487893 12.17574 #5 5.183255 6.570061 8.50582 15.01856 5.479091542 5.41808 #6 10.33686 9.944028 11.11948 10.94331 11.7681534 9.070998 #7 8.934657 6.698508 8.634132 7.722149 7.583802292 9.275215 #8 18.5432 16.09926 17.17215 16.93843 15.6574951 13.2776 #9 49.97551 17.9049 21.29354 29.06767 16.31443679 11.18214 #10 11.20804 8.221766 10.68665 12.20551 8.390061493 10.80571 Average 14.82967 10.86644 12.81125 15.46629 11.32623153 9.768222 Table 2. The experimental results in terms of Cumulative Variation of RootMean Square Error (CV-RMSE)given inpercentages. Test SVM RF DT MLP LSTM DeepEnergy #1 9.058992 9.423908 10.57686 10.65546 12.16246177 8.948922 #2 10.14701 10.63412 12.99834 13.91199 12.19377007 10.46165 #3 17.02552 12.42314 14.58249 23.2753 16.9291218 13.30116 #4 21.22162 21.1038 24.48298 23.63544 14.13596516 14.63439 #5 6.690527 7.942747 10.10017 15.44461 6.334195125 6.653999 #6 11.88856 11.6989 13.39033 12.20149 12.96057349 10.74021 #7 10.77881 7.871596 10.35254 8.716806 8.681353107 10.85454 #8 19.49707 17.09079 18.95726 17.73124 16.55737557 14.51027 #9 54.58171 19.91185 24.84425 29.37466 17.66342548 13.01906 #10 13.80167 10.15117 13.06351 13.39278 10.20235927 13.47003 Average 17.46915 12.8252 15.33487 16.83398 12.78206008 11.65942 426