Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Abstract
:1. Introduction
2. PV Module Description and Database
2.1. PV Module Description
- -
- PV technology: Silicon mono crystalline
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- Rated power: 285 Wp
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- Azimuth: 6°30’ (assuming 0° as South direction and counting clockwise)
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- Solar panel tilt angle (β): 30°
2.2. Performance Indexes
2.3. Database Clustering
- Sunny days: these are characterized by a mean value of the solar irradiance during 24 h greater than 150 W/m² (i.e., );
- Cloudy days: these are characterized by a mean value of solar irradiance in the range [5–150 W/m²] (i.e., ).
3. Methodology
- Ap1: All the available data are used to train the network (268 days)
- Ap2: The simulations are performed using the dataset comprising all the available data but, in order to train the network, the same number of days available in the sunny and cloudy dataset is used (randomly picked)
- Ap3: The simulations are performed using the “Sunny” and “Cloudy” dataset alternatively.
4. Results and Discussion
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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NMAE | EMAE | WMAE | nRMSE | OMAE | |
---|---|---|---|---|---|
NMAE | 1 | 0.59 | 0.37 | 0.40 | 0.90 |
EMAE | 1 | 0.80 | 0.78 | 0.71 | |
WMAE | 1 | 0.98 | 0.48 | ||
nRMSE | 1 | 0.51 | |||
OMAE | 1 |
Imposed Threshold on GPOA,f,d | 150 (W/m2) |
---|---|
# of days | 268 |
# of sunny days | 154 |
# of cloudy days | 114 |
Sunny | Day 5 | Day 15 | Day 16 | Day 18 | Day 20 | Day 21 |
---|---|---|---|---|---|---|
Case 1 | 10.83 | 12.89 | 10.45 | 7.17 | 9.51 | 9.61 |
Case 2 | 14.95 | 21.43 | 30.39 | 1.96 | 2.22 | 3.29 |
Cloudy | Day 11 | Day 12 | Day 13 | Day 14 | Day 17 | Day 19 |
---|---|---|---|---|---|---|
Case 1 | 156.21 | 143.15 | 57.86 | 26.51 | 42.63 | 42.01 |
Case 2 | 750.01 | 35.87 | 41.26 | 31.6 | 38.14 | 9.38 |
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Nespoli, A.; Ogliari, E.; Leva, S.; Massi Pavan, A.; Mellit, A.; Lughi, V.; Dolara, A. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies 2019, 12, 1621. https://doi.org/10.3390/en12091621
Nespoli A, Ogliari E, Leva S, Massi Pavan A, Mellit A, Lughi V, Dolara A. Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies. 2019; 12(9):1621. https://doi.org/10.3390/en12091621
Chicago/Turabian StyleNespoli, Alfredo, Emanuele Ogliari, Sonia Leva, Alessandro Massi Pavan, Adel Mellit, Vanni Lughi, and Alberto Dolara. 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques" Energies 12, no. 9: 1621. https://doi.org/10.3390/en12091621
APA StyleNespoli, A., Ogliari, E., Leva, S., Massi Pavan, A., Mellit, A., Lughi, V., & Dolara, A. (2019). Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies, 12(9), 1621. https://doi.org/10.3390/en12091621