Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network
Abstract
:1. Introduction
2. Methods
2.1. The HCPV Facility
2.2. Artificial Neural Networks
2.3. Proposed Prediction Model for Very-Short-Term Power Prediction
3. Results and Analysis
4. Conclusions and Future Works
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial intelligence |
AM | Air Mass |
ANFIS | Adaptive Neuro Fuzzy Inference System |
ANN | Artificial Neural Network |
APE | Average Photon Energy |
ARIMA | AutoRegressive Integrated Moving Average |
ARMA | AutoRegressive Moving Average |
ARX | Auto Regressive Exogenous |
CPV | Concentrator Photovoltaics |
DHI | Diffuse Irradiance |
DNI | Direct Normal Irradiance |
FFNN | Feed-Forward Neural Network |
GA | Genetic Algorithms |
HCPV | High Concentrator Photovoltaics |
MAE | Mean Absolute Error |
MAPE | Mean Absolute Percentage Error |
MBE | Mean Bias Error |
MJ | Multi-Junction |
MSE | Mean Squared Error |
PCA | Principal Component Analysis |
PMMA | PolyMethyl MetaAcrylate |
PSO | Particle Swarm Optimisation |
PV | Photovoltaics |
PW | Precipitable Water |
RBF | Radial Basis Functions |
RBFNN | Radial Basis Functions Neural Network |
RMS | Root Mean Square |
RMSE | Root Mean Square Error |
SVM | Support Vector Machine |
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Term | Forecast Horizon Range | Application |
---|---|---|
Very Short | Few seconds–30 min | Control & adjustment actions |
Short | 30 min–6 h | Dispatch planning; load gain/drop |
Medium | 6 h–1 day | Generator On/Off, operational security, electricity market |
Long | 1 day–1 week | Unit commitment, reserve requirement, maintenance schedule |
Primary optics | 310 mm × 310 mm Fresnel lens |
Secondary optics | Refractive truncated pyramid |
Geometrical concentration | ×961 |
Concentration | ×800 |
Cell type | 10 mm × 10 mm Lattice-matched GaInP/GaInAs/Ge |
Protection type of the cell | Bypass diode |
The number of cells per module | 6 in series |
Module max. power | 110 W |
Open-circuit voltage | 17.70 V |
Short-circuit current | 8.65 A |
Cooling mechanism | Passive |
Model | Nodes | RMSE 1 Steps Ahead | RMSE1/RMS(P) | RMSE 15 Steps Ahead | RMSE15/RMS(P) |
---|---|---|---|---|---|
1 | 21 | 0.0668 | 0.0909 | 0.1779 | 0.2222 |
2 | 21 | 0.0670 | 0.0912 | 0.1909 | 0.2598 |
3 | 24 | 0.0671 | 0.0913 | 0.1889 | 0.2571 |
4 | 18 | 0.0674 | 0.0917 | 0.1729 | 0.2354 |
5 | 24 | 0.0666 | 0.0907 | 0.1861 | 0.2534 |
Model Number | Steps | MBE | MAE | MAPE | MSE |
---|---|---|---|---|---|
1 | 1 | −0.0253 × | 0.0245 | 10.4 | 4458 × |
15 | 0.0548 | 0.1253 | 28.4 | 0.0316 | |
2 | 1 | 0.0626 × | 0.0250 | 10.7 | 4485 × |
15 | 0.0552 | 0.1330 | 30.01 | 0.0364 | |
3 | 1 | 0.0673 × | 0.0252 | 11.1 | 4500 × |
15 | 0.0673 | 0.1325 | 29.9 | 0.0357 | |
4 | 1 | 0.1762 × | 0.0251 | 11.4 | 4541 × |
15 | 0.0686 | 0.1252 | 29.8 | 0.0299 | |
5 | 1 | 0.0607 × | 0.0247 | 10.6 | 4436 × |
15 | 0.0604 | 0.1253 | 30.8 | 0.0346 |
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Alamin, Y.I.; Anaty, M.K.; Álvarez Hervás, J.D.; Bouziane, K.; Pérez García, M.; Yaagoubi, R.; Castilla, M.d.M.; Belkasmi, M.; Aggour, M. Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network. Energies 2020, 13, 3493. https://doi.org/10.3390/en13133493
Alamin YI, Anaty MK, Álvarez Hervás JD, Bouziane K, Pérez García M, Yaagoubi R, Castilla MdM, Belkasmi M, Aggour M. Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network. Energies. 2020; 13(13):3493. https://doi.org/10.3390/en13133493
Chicago/Turabian StyleAlamin, Yaser I., Mensah K. Anaty, José Domingo Álvarez Hervás, Khalid Bouziane, Manuel Pérez García, Reda Yaagoubi, María del Mar Castilla, Merouan Belkasmi, and Mohammed Aggour. 2020. "Very Short-Term Power Forecasting of High Concentrator Photovoltaic Power Facility by Implementing Artificial Neural Network" Energies 13, no. 13: 3493. https://doi.org/10.3390/en13133493