Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas
Abstract
:1. Introduction
2. Data and Methodology
2.1. Data
2.1.1. Ground Measurements
2.1.2. Earth Observations
2.2. Methodology: Energy Management Systems
2.2.1. Solar Energy Nowcasting SystEm (SENSE)
2.2.2. Indian Solar Irradiance Operational System (INSIOS)
2.2.3. Error Analysis
3. Results
3.1. Reliability of INSIOS
3.2. Comparison Against BSRN
4. Discussion
4.1. Sensitivity Analysis
4.1.1. Effect of Cloud
4.1.2. Effect of Aerosol
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Station | Code | Latitude (°) | Longitude (°) | Elevation (m) | Population (million) |
---|---|---|---|---|---|
Gandhinagar | GAN | 23.11 | 72.63 | 65 | 0.21 |
Delhi | DEL | 28.42 | 77.16 | 259 | 11.03 |
Howrah | HOW | 22.55 | 88.31 | 51 | 1.08 |
Tiruvallur | TIR | 13.09 | 79.97 | 36 | 0.56 |
GHIclear (W/m2) | GHIcloudy (W/m2) | DNIclear (W/m2) | DNIcloudy (W/m2) | |
---|---|---|---|---|
957.75 | 1383.85 | 906.94 | 0 | |
−3.96 | −15.20 | −2.00 | 0 | |
−122.29 | −120.13 | −267.32 | 0 | |
0.19 | 0.06 | 3.41 × 10−2 | 0 | |
3.95 | 1.67 | 0.14 | 0 | |
0 | 6.33 | 0 | 0 | |
−1.39 × 10−2 | −2.39 × 10−3 | −7.81 × 10−3 | 1.70 × 10−6 | |
−0.27 | −1.69 × 10−2 | −8.14 × 10−2 | 0 | |
14.04 | −5.71 × 10−2 | 13.23 | 0 | |
0 | −0.21 | 0 | 0 | |
2.81 × 10−4 | 5.38 × 10−5 | 1.67 × 10−4 | −1.52 × 10−8 | |
7.76 × 10−3 | 2.57 × 10−5 | 4.18 × 10−3 | 4.35 × 10−8 | |
−0.37 | 7.32 × 10−4 | −0.48 | −6.33 × 10−7 | |
−42.60 | 5.48 × 10−4 | −10.42 | −4.75 × 10−6 | |
0 | 4.40 × 10−3 | 0 | 1.54 × 10−5 | |
−2.70 × 10−6 | −5.56 × 10-07 | −1.67 × 10−6 | −5.21 × 10−11 | |
-9.31 × 10−5 | 1.40 × 10−6 | −6.77 × 10−5 | 3.46 × 10−9 | |
1.70 × 10−4 | −6.29 × 10−6 | 6.94 × 10−3 | −4.45 × 10−8 | |
1.47 | −4.17 × 10−7 | 0.25 | 2.49 × 10−7 | |
0 | −4.97 × 10−6 | 0 | −3.54 × 10−8 | |
0 | −5.09 × 10−5 | 0 | −8.21 × 10−7 | |
1.03 × 10−8 | 7.74 × 10−10 | 6.68 × 10−9 | 1.81 × 10−13 | |
3.95 × 10−7 | 9.76 × 10−9 | 3.87 × 10−7 | −5.33 × 10−12 | |
3.67 × 10−5 | −6.86 × 10−8 | −3.36 × 10−5 | −1.00 × 10−11 | |
−1.40 × 10−2 | 2.14 × 10−7 | −2.03 × 10−3 | 1.04 × 10−9 | |
0.242755 | −2.82 × 10−7 | 4.80 × 10−2 | −8.14 × 10−9 | |
0 | 1.99 × 10−7 | 0 | 7.13 × 10−9 | |
0 | 2.06 × 10−7 | 0 | 1.41 × 10−8 | |
0 | 0 | 0 | −5.76 × 10−17 | |
0 | 0 | 0 | −1.72 × 10−15 | |
0 | 0 | 0 | 1.07 × 10−13 | |
0 | 0 | 0 | −1.08 × 10−12 | |
0 | 0 | 0 | −2.41 × 10−12 | |
0 | 0 | 0 | 6.38 × 10−11 | |
0 | 0 | 0 | −8.69 × 10−11 | |
0 | 0 | 0 | −7.48 × 10−11 |
Station | Season | MBE (W/m2) | RMSE (W/m2) | rMBE (%) | rRMSE (%) | MBE (W/m2) | RMSE (W/m2) | rMBE (%) | rRMSE (%) |
---|---|---|---|---|---|---|---|---|---|
GHI | |||||||||
Clear-Sky | Cloudy | ||||||||
DEL | Winter | −51.1 | 76.2 | −16.7 | 24.9 | 109.7 | 152.2 | 56.5 | 78.4 |
Summer | −86.7 | 130.2 | −18.5 | 27.7 | 345.9 | 384.5 | 55.1 | 61.2 | |
Monsoon | −143.3 | 206.5 | −34.7 | 50.0 | 192.6 | 279.4 | 42.2 | 61.2 | |
Autumn | −65.5 | 85.9 | −18.8 | 24.6 | 239.5 | 271.6 | 61.0 | 69.1 | |
Annual | −89.5 | 139.1 | −22.3 | 34.6 | 210.7 | 280.2 | 50.2 | 66.8 | |
GAN | Monsoon | −151.1 | 213.2 | −41.4 | 58.4 | 217.3 | 297.0 | 46.0 | 62.8 |
HOW | Winter | −69.1 | 88.9 | −20.6 | 26.5 | 203.2 | 243.6 | 57.9 | 69.5 |
Monsoon | −136.0 | 204.3 | −33.3 | 49.9 | −61.8 | 78.5 | −18.5 | 23.5 | |
Autumn | −95.0 | 132.4 | −24.7 | 34.4 | −35.7 | 47.4 | −9.9 | 13.2 | |
Annual | −95.1 | 139.2 | −25.4 | 37.2 | −55.5 | 70.5 | −16.3 | 20.7 | |
TIR | Winter | −48.7 | −86.9 | −150.2 | −94.4 | 182.3 | 250.6 | 48.2 | 66.2 |
Summer | 140.9 | 167.6 | 227.5 | 180.7 | 389.6 | 427.3 | 57.5 | 63.1 | |
Monsoon | −12.5 | −19.7 | −42.1 | −23.5 | 228.7 | 308.4 | 45.7 | 61.6 | |
Annual | 36.0 | 38.1 | 63.8 | 45.1 | 275.1 | 344.8 | 51.2 | 64.2 | |
DNI | |||||||||
Clear-Sky | Cloudy | ||||||||
DEL | Winter | −8.1 | 151.6 | −2.3 | 42.7 | 2.1 | 3.1 | 77.5 | 116.3 |
Summer | −173.8 | 268.6 | −45.4 | 70.2 | 0.8 | 0.8 | 75.0 | 75.1 | |
Monsoon | −226.4 | 309.6 | −69.6 | 95.2 | 1.4 | 1.7 | 83.4 | 98.9 | |
Autumn | −72.7 | 140.0 | −23.3 | 45.0 | 2.5 | 2.7 | 84.3 | 88.5 | |
Annual | −127.0 | 234.8 | −36.2 | 66.9 | 1.8 | 2.5 | 80.0 | 112.6 | |
GAN | Monsoon | −144.3 | 224.4 | −40.5 | 63.0 | 1.7 | 1.9 | 83.7 | 93.6 |
HOW | Winter | −70.6 | 126.0 | −24.1 | 42.9 | 0.5 | 0.5 | 53.0 | 53.0 |
Monsoon | −215.9 | 310.4 | −65.7 | 94.4 | 1.3 | 1.5 | 81.3 | 94.0 | |
Autumn | −91.1 | 165.1 | −26.4 | 47.9 | 1.7 | 2.0 | 82.8 | 101.6 | |
Annual | −100.6 | 180.3 | −31.3 | 56.1 | 1.4 | 1.8 | 81.1 | 100.0 | |
TIR | Winter | −10.9 | 164.0 | −4.1 | 61.4 | 1.7 | 2.2 | 75.0 | 97.3 |
Summer | −165.5 | 267.6 | −58.9 | 95.2 | 1.6 | 1.7 | 80.1 | 86.4 | |
Monsoon | −226.9 | 330.0 | −81.1 | 118.0 | 1.6 | 1.9 | 76.9 | 88.6 | |
Annual | −125.0 | 253.6 | −45.3 | 91.9 | 1.6 | 2.0 | 75.7 | 94.7 |
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Masoom, A.; Kosmopoulos, P.; Bansal, A.; Kazadzis, S. Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas. Remote Sens. 2020, 12, 254. https://doi.org/10.3390/rs12020254
Masoom A, Kosmopoulos P, Bansal A, Kazadzis S. Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas. Remote Sensing. 2020; 12(2):254. https://doi.org/10.3390/rs12020254
Chicago/Turabian StyleMasoom, Akriti, Panagiotis Kosmopoulos, Ankit Bansal, and Stelios Kazadzis. 2020. "Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas" Remote Sensing 12, no. 2: 254. https://doi.org/10.3390/rs12020254
APA StyleMasoom, A., Kosmopoulos, P., Bansal, A., & Kazadzis, S. (2020). Solar Energy Estimations in India Using Remote Sensing Technologies and Validation with Sun Photometers in Urban Areas. Remote Sensing, 12(2), 254. https://doi.org/10.3390/rs12020254