Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model
Abstract
:1. Introduction
Contribution
2. Methodology
2.1. Data
2.2. Proposed Algorithm
2.2.1. NSGA II
2.2.2. MLPNN
2.2.3. ANFIS
2.2.4. Genetic Algorithm (GA)
3. Results and Discussion
3.1. Primary Forecasting Step
3.2. Final Forecasting Step
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
ACO | Ant Colony Optimization |
ANFIS | Adaptive Neuro-Fuzzy Inference System |
ANN | Artificial Neural Network |
APE | Absolute Percentage Error |
ARIMA | Auto-Regressive Integrated Moving Average |
DE | Differential Evolution |
FIS | Fuzzy Inference System |
GA | Genetic Algorithm |
ICA | Imperialistic Competitive Algorithm |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
MF | Membership Function |
MLPNN | Multi-Layer Perceptron Neural Network |
NSGA II | Non-dominated Sorting Genetic Algorithm II |
PSO | Particle Swarm Optimization |
R | Correlation Coefficient |
RMSE | Root Mean Square Error |
TSK | Takagi-Sugeno-Kang |
WT | Wavelet Transform |
Appendix A
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Input Set | MAE (MW) | R | |||
---|---|---|---|---|---|
1 input | 301.7724 | 3.8788 | 195.0414 | 3.1479 | 0.9523 |
Selected inputs by NSGA II | 136.1048 | 1.7409 | 68.3688 | 1.0950 | 0.9904 |
All Inputs | 160.5449 | 2.0636 | 69.4196 | 1.1029 | 0.9866 |
Model | MAE (MW) | R | |||
---|---|---|---|---|---|
ANFIS-ACOR | 207.2688 | 2.8755 | 98.9818 | 1.5777 | 0.9775 |
ANFIS-Hybrid | 194.6117 | 2.4017 | 86.8079 | 1.3838 | 0.9803 |
ANFIS-DE | 202.3010 | 2.1925 | 100.7624 | 1.6181 | 0.9795 |
ANFIS-GA | 190.1248 | 2.1810 | 97.4025 | 1.5438 | 0.9820 |
ANFIS-ICA | 288.7683 | 3.8939 | 208.4114 | 3.3892 | 0.9577 |
ANFIS-PSO | 195.7518 | 2.1215 | 83.5948 | 1.3360 | 0.9805 |
Model | MAE (MW) | R | |||
---|---|---|---|---|---|
MLPNN-ANFIS-ACOR | 175.1901 | 1.8987 | 69.1994 | 1.1033 | 0.9842 |
MLPNN-ANFIS-Hybrid | 142.6229 | 1.8243 | 66.5694 | 1.0603 | 0.9896 |
MLPNN-ANFIS-DE | 158.2172 | 1.9138 | 68.2244 | 1.0967 | 0.9869 |
MLPNN-ANFIS-GA | 107.2644 | 1.5063 | 65.4250 | 1.0570 | 0.9940 |
MLPNN-ANFIS-ICA | 121.7895 | 1.5229 | 65.5095 | 1.0639 | 0.9922 |
MLPNN-ANFIS-PSO | 148.1894 | 2.0559 | 66.8190 | 1.0641 | 0.9886 |
Model | MAE (MW) | R | |||
---|---|---|---|---|---|
MLPNN | 136.1048 | 1.7409 | 68.3688 | 1.0950 | 0.9904 |
ANFIS-Hybrid | 194.6117 | 2.4017 | 86.8079 | 1.3838 | 0.9803 |
ANFIS-GA | 190.1248 | 2.1810 | 97.4025 | 1.5438 | 0.9820 |
MLPNN-ANFIS-GA | 107.2644 | 1.5063 | 65.4250 | 1.0570 | 0.9940 |
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Jadidi, A.; Menezes, R.; de Souza, N.; de Castro Lima, A.C. Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model. Energies 2019, 12, 1891. https://doi.org/10.3390/en12101891
Jadidi A, Menezes R, de Souza N, de Castro Lima AC. Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model. Energies. 2019; 12(10):1891. https://doi.org/10.3390/en12101891
Chicago/Turabian StyleJadidi, Aydin, Raimundo Menezes, Nilmar de Souza, and Antonio Cezar de Castro Lima. 2019. "Short-Term Electric Power Demand Forecasting Using NSGA II-ANFIS Model" Energies 12, no. 10: 1891. https://doi.org/10.3390/en12101891