Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation
<p>Schematic diagram of the sun’s position.</p> "> Figure 2
<p>Relationship between solar angles and the PV power station.</p> "> Figure 3
<p>A PV power forecasting model integrating radar data and sky imager data.</p> "> Figure 4
<p>Variations in solar altitude and azimuth angles in Guizhou on 27 June 2024.</p> "> Figure 5
<p>Installation location of the sky imager.</p> "> Figure 6
<p>Comparison of forecasted irradiance, observed irradiance, mean of MCR, and actual power output.</p> "> Figure 7
<p>A precipitation event displayed by MCR data (1 July 2024).</p> "> Figure 8
<p>Sky region extraction from sky imager.</p> "> Figure 9
<p>Trajectory of the sun on the sky imager.</p> "> Figure 10
<p>Positions of the sun in captured images at different times.</p> "> Figure 11
<p>Relationships between cloud cover data (global sky and sun-centric) and observed irradiance (1 July–8 July 2024).</p> "> Figure 12
<p>Comparison of actual and predicted power output.</p> ">
Abstract
:1. Introduction
2. Related Work
2.1. Determination of the Positions of the Sun, Sky Imager, and PV Power Station
- (1)
- The solar altitude angle is calculated using the following equation:
- (2)
- Calculation of the solar azimuth angle
- (i)
- When , the solar azimuth angle is calculated using the following equation:
- (ii)
- When , the solar azimuth angle is calculated as follows:
2.2. Determination of Solar Occlusion Area Using Sky Imager Images
3. Proposed Methodology
3.1. Specific Steps of the Implementation Method
3.2. Key Issues Discussion
3.2.1. Determining the Relative Positions of the Sun, Sky Imager, and PV Station
3.2.2. Influence of Radar Data and Rainfall Changes on Power Prediction
3.3. Model Performance Evaluation
- (1)
- MSE
- (2)
- (3)
- Theil’s U Statistic
- (4)
- NSE
3.4. Evaluation Criteria for Ultra-Short-Term Forecasting Models
4. Experimental and Discussion
4.1. Experimental Setup
4.2. Experimental Results
4.2.1. Impact of MCR Data on the Accuracy of Ultra-Short-Term Power Forecasting
4.2.2. Estimation of Partial Cloud Coverage in the Sun’s Position
4.2.3. Correction of NWP Data and Performance Evaluation of the Power Prediction Model
5. Conclusions and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Longitude & Latitude | Altitude | Installed Capacity | Sky Imager Size | Radar Image Resolution | Time Span |
---|---|---|---|---|---|
(105.471° E, 25.856° N) | 1000 m | 200 MW | 3073 × 2048 pixels | 1 × 1 km | 1 June–8 July |
Item | Technical Parameters |
---|---|
Main Unit | Embedded computer host/auto exposure |
Field of View | 180° |
Image Resolution and Storage Format | 1600 × 1200 pixels, JPEG |
Sampling Frequency | Adjustable (fastest: 30 s per image) |
Data Size | 60 M per day (if capturing every 10 min) |
Operating System | Win XP, Vista, Win 7 and Win10 |
Communication Method | Standard Ethernet (TCP/IP) |
Software | Distributed control software, capable of controlling multiple imagers via a server, with data storage, display, and post-processing functions. |
Protection | Built-in waterproof ventilation system |
Dimensions and Weight | 20 × 20 × 20 cm/3 kg |
Sunshade (Optional) | Large-size sunshade |
Power Supply and Consumption | 12 V/1 A (Heater on/off: 3 W/6 W) |
Cloud Measurement Distance | 0–10 km |
Cloud Coverage Range | 0~100% |
Resolution | 1% |
Visible Light Pixel | ≥1 million |
Infrared Detection Wavelength | 8–14 μm |
Detection Temperature Range | −80 °C to +25 °C |
Reporting Cycle | 10 min |
Measurement Accuracy | ±20% (horizontal) |
Visibility | ≥2 km |
Date | Accuracy (%) | MSE | Theil’s U | NSE | |
---|---|---|---|---|---|
1 July | 23.50 | 1374.39 | 0.26 | 0.73 | 0.27 |
2 July | 74.13 | 687.42 | 0.77 | 0.37 | 0.79 |
3 July | 54.52 | 372.76 | 0.51 | 0.59 | 0.50 |
4 July | 72.88 | 261.86 | 0.85 | 0.31 | 0.86 |
5 July | 73.79 | 170.92 | 0.79 | 0.40 | 0.80 |
6 July | 80.19 | 358.71 | 0.86 | 0.32 | 0.85 |
7 July | 82.90 | 285.59 | 0.89 | 0.29 | 0.88 |
8 July | 76.19 | 340.32 | 0.86 | 0.31 | 0.85 |
Input Features | Output Features | Machine Learning Algorithm | MSE | Theil’s U | NSE | |
---|---|---|---|---|---|---|
Include All-sky cloud cover data | Actual radiation | Random Forest | 21,923.67 | 0.63 | 0.49 | 0.67 |
Xgboost | 25,720.44 | 0.60 | 0.53 | 0.63 | ||
Actual power output | Random Forest | 158.06 | 0.93 | 0.24 | 0.95 | |
Xgboost | 168.10 | 0.92 | 0.24 | 0.93 | ||
Include Sun-region cloud cover data | Actual radiation | Random Forest | 14,135.67 | 0.78 | 0.39 | 0.80 |
Xgboost | 15,140.20 | 0.75 | 0.41 | 0.78 | ||
Actual power output | Random Forest | 143.84 | 0.96 | 0.23 | 0.95 | |
Xgboost | 162.98 | 0.94 | 0.24 | 0.95 |
Date | Accuracy (%) | MSE | Theil’s U | NSE | |
---|---|---|---|---|---|
1 July | 75.14 | 501.65 | 0.75 | 0.44 | 0.76 |
2 July | 75.37 | 605.23 | 0.80 | 0.35 | 0.81 |
3 July | 70.76 | 172.20 | 0.81 | 0.40 | 0.79 |
4 July | 77.34 | 204.66 | 0.89 | 0.27 | 0.90 |
5 July | 73.31 | 201.79 | 0.83 | 0.34 | 0.82 |
6 July | 82.16 | 305.54 | 0.89 | 0.29 | 0.88 |
7 July | 83.72 | 212.43 | 0.92 | 0.25 | 0.91 |
8 July | 81.97 | 137.59 | 0.95 | 0.21 | 0.94 |
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Wu, W.; Gao, R.; Wu, P.; Yuan, C.; Xia, X.; Liu, R.; Wang, Y. Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation. Energies 2025, 18, 28. https://doi.org/10.3390/en18010028
Wu W, Gao R, Wu P, Yuan C, Xia X, Liu R, Wang Y. Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation. Energies. 2025; 18(1):28. https://doi.org/10.3390/en18010028
Chicago/Turabian StyleWu, Weixiong, Rui Gao, Peng Wu, Chen Yuan, Xiaoling Xia, Renfeng Liu, and Yifei Wang. 2025. "Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation" Energies 18, no. 1: 28. https://doi.org/10.3390/en18010028
APA StyleWu, W., Gao, R., Wu, P., Yuan, C., Xia, X., Liu, R., & Wang, Y. (2025). Enhanced Ultra-Short-Term PV Forecasting Using Sky Imagers: Integrating MCR and Cloud Cover Estimation. Energies, 18(1), 28. https://doi.org/10.3390/en18010028