Page - 59 - in Short-Term Load Forecasting by Artificial Intelligent Technologies

Energies2019,12, 164 30 40 50 60 70 80 90 100 110 120 Historical data (number of samples) 1 1.5 2 2.5 3 3.5 4 4.5 5 MI+ANN forecast Bi-level forecast AMI+ANN+mEDE forecast (a)Errorperformance 30 40 50 60 70 80 90 100 110 120 Historical data (number of samples) 0 20 40 60 80 100 120 MI+ANN forecast Bi-level forecast MI+ANN+mEDE forecast (b)Convergencerateanalysis Figure7.Relativescalabilityanalysisof theproposedintelligentmodularapproach. Table3.EKPC:Results for January2015. Day ForecastModel MI+ANN Bi-Level MI+ANN+mEDE MAPE Variance MAPE Variance MAPE Variance 1 3.99 1.89 2.40 1.50 1.04 1.12 2 3.42 1.78 1.97 1.46 1.32 0.97 3 4.10 2.08 2.61 1.26 1.15 1.09 4 3.67 1.91 2.13 1.41 1.44 0.96 5 3.79 1.70 1.97 1.37 1.16 1.05 6 3.62 1.88 2.43 1.48 1.29 0.97 7 3.93 1.73 2.62 1.39 1.40 1.11 8 3.97 1.94 1.92 1.28 1.19 1.03 9 3.54 2.04 2.18 1.42 1.39 0.90 10 3.46 1.79 2.21 1.36 1.10 1.03 11 4.05 1.72 1.85 1.39 1.25 1.05 12 4.21 1.84 1.97 1.29 1.29 0.90 13 3.89 2.00 1.94 1.33 1.07 1.03 14 3.62 1.75 1.84 1.46 1.36 1.10 15 3.79 1.99 2.11 1.26 1.14 0.93 16 3.47 1.81 2.44 1.38 1.36 1.07 17 4.24 2.10 2.26 1.26 1.20 1.04 18 4.20 1.74 2.61 1.41 1.23 1.08 19 3.86 1.97 2.44 1.46 1.07 0.96 20 3.61 1.80 2.52 1.42 1.18 0.98 21 3.82 1.95 2.29 1.48 1.36 1.12 22 3.77 2.03 2.62 1.45 1.42 0.99 23 4.23 1.86 2.53 1.51 1.34 1.01 24 3.94 1.77 2.38 1.29 1.11 0.92 25 3.44 1.73 2.20 1.47 1.32 1.14 26 3.56 1.94 2.23 1.34 1.10 0.97 27 3.81 1.78 2.29 1.40 1.24 1.11 28 3.39 1.82 1.94 1.29 1.39 1.03 29 4.19 2.05 2.43 1.32 1.08 0.98 30 3.52 1.77 1.98 1.42 1.12 1.06 31 4.01 1.99 1.82 1.42 1.33 0.99 Average 3.81 1.84 2.23 1.38 1.24 1.03 59