Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms
Abstract
:1. Introduction
2. Literature Review
3. Heuristic Algorithms
3.1. Gravitational Search Algorithm
3.2. Cuckoo Optimization Algorithm
4. Research Design
4.1. Historical Data
4.2. Structure of the Neural Network
4.3. Training Neural Networks by Heuristic Algorithms
4.4. Examining the Performance
5. Experimental Results and Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Factor | |
---|---|
x1 | Month index |
x2 | Average air pressure |
x3 | Average temperature |
x4 | Average wind velocity |
x5 | Rainfall |
x6 | Rainy time |
x7 | Average relative humidity |
x8 | Daylight time |
No. of Iterations | Model | MAPE | RMSE | MAE | R |
---|---|---|---|---|---|
500 | ANN | 0.1472 | 137,208 | 121,152 | 0.7206 |
ANN-GSA | 0.0848 | 86,080 | 73,540 | 0.8415 | |
ANN-COA | 0.0843 | 85,118 | 73,108 | 0.8709 | |
ANN-DE | 0.1462 | 136,819 | 119,261 | 0.7608 | |
1000 | ANN | 0.0826 | 78,652 | 120,242 | 0.7932 |
ANN-GSA | 0.0764 | 75,148 | 66,267 | 0.8779 | |
ANN-COA | 0.0577 | 59,073 | 49,238 | 0.9287 | |
ANN-DE | 0.0671 | 68,325 | 56,409 | 0.9332 | |
1500 | ANN | 0.0803 | 75,524 | 93,142 | 0.7988 |
ANN-GSA | 0.0759 | 74,218 | 63,733 | 0.8842 | |
ANN-COA | 0.0578 | 57,731 | 49,100 | 0.9372 | |
ANN-DE | 0.0633 | 65,529 | 56, 937 | 0.9382 | |
2000 | ANN | 0.0796 | 73,482 | 76,249 | 0.8020 |
ANN-GSA | 0.0731 | 72,980 | 64,267 | 0.8892 | |
ANN-COA | 0.0478 | 53,308 | 48,238 | 0.9386 | |
ANN-DE | 0.0582 | 64,372 | 55,021 | 0.8921 |
Method | MAPE | RMSE | MAE | R |
---|---|---|---|---|
MLR | 0.1662 | 170,540 | 145,910 | 0.6031 |
ARIMA (2,1,1) | 0.1603 | 152,070 | 136,260 | 0.7043 |
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Chen, J.-F.; Lo, S.-K.; Do, Q.H. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information 2017, 8, 31. https://doi.org/10.3390/info8010031
Chen J-F, Lo S-K, Do QH. Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information. 2017; 8(1):31. https://doi.org/10.3390/info8010031
Chicago/Turabian StyleChen, Jeng-Fung, Shih-Kuei Lo, and Quang Hung Do. 2017. "Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms" Information 8, no. 1: 31. https://doi.org/10.3390/info8010031
APA StyleChen, J.-F., Lo, S.-K., & Do, Q. H. (2017). Forecasting Monthly Electricity Demands: An Application of Neural Networks Trained by Heuristic Algorithms. Information, 8(1), 31. https://doi.org/10.3390/info8010031