An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting
Abstract
:1. Introduction
- A novel feature selection technique was employed to investigate the feature patterns;
- A novel hybrid algorithm was explored for PV power forecasting;
- A fair evaluation was presented by showing the numerical and graphical performances of the proposed hybrid model.
2. Background and Proposed Architecture
2.1. Symbolic Regression
2.2. Deep Multi-Layer Perceptron
2.3. Genetic Programming
2.4. Problem Formulation
3. Hybrid Model
4. Case Study
4.1. Features Selection
4.2. Training and Simulation Results
4.3. Discussions
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Model | Reference | Score Metrics | Lowest Score | Dataset |
---|---|---|---|---|
XGBF -DNN | [24] | RMSE, MBE , FS | RMSE = 51.35 W | PV data in Limberg, Belgium |
SR-FFNN | [25] | RMSE, MBE , MAE, | = 0.932 | Solar power in Flanders, Belgium |
LSTM | [26] | NMAE, RMSE | RMSE = 38.13 kWh | 1 MW PV site in Goheung, Korea |
Modified LSTM | [27] | MAE, RMSE | RMSE = 0.55 kW | Ansan, Gyeonggi-do, Korea |
LSTM-EMA | [28] | RMSE, , MAPE | = 0.96 | Yeonseong-gun, Gyeonggi-do, South Korea |
ENS | [29] | NRMSE, nMBE, MAE, nMAE | MAE = 74.1 kW | 32 PV plants installed at different latitudes in Italy |
GA-PSO-ANFIS | [30] | RMSE, MAE, NMAE, FS | RMSE = 2.08 kW | Goldwind microgrid system found in Beijing |
SOM , LVQ , SVR | [31] | MRE and RMSE | MRE = 1.79% | Taiwan Central Weather Bureau |
PFLRM | [32] | RMSE, MAD , MAPE | RMSE = 59.38 kW | Coloane island of Macau |
ANN | [33] | RMSE, | = 0.999 | Solar power plant in Dhaka |
LSH | [34] | RMSE, MRE, QR | RMSE = 4.23 kW | PV power station in Ashland |
AE -LSTM | [35] | MAPE, RMSE, MAE | RMSE = 0.14 kW | PV inverter installed in Haenam, South Korea |
SFLA -ANN | [36] | MAPE | MAPE = 5.38% | PV sites in Florida |
PCPOW | [37] | = 0.938 | Yunnan Electric Power Research Institute |
Errors | SR | MLP | SR-MLP |
---|---|---|---|
RMSE (kW) | 7.21 | 6.48 | 5.58 |
MAE (kW) | 4.92 | 3.81 | 3.3 |
0.988 | 0.990 | 0.993 |
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Trabelsi, M.; Massaoudi, M.; Chihi, I.; Sidhom, L.; Refaat, S.S.; Huang, T.; Oueslati, F.S. An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting. Energies 2022, 15, 9008. https://doi.org/10.3390/en15239008
Trabelsi M, Massaoudi M, Chihi I, Sidhom L, Refaat SS, Huang T, Oueslati FS. An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting. Energies. 2022; 15(23):9008. https://doi.org/10.3390/en15239008
Chicago/Turabian StyleTrabelsi, Mohamed, Mohamed Massaoudi, Ines Chihi, Lilia Sidhom, Shady S. Refaat, Tingwen Huang, and Fakhreddine S. Oueslati. 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting" Energies 15, no. 23: 9008. https://doi.org/10.3390/en15239008