Reliability Predictors for Solar Irradiance Satellite-Based Forecast
Abstract
:1. Introduction
- helping dispatch operators to propose the cheapest and safest electric mix according to user’s consumption and energy availability.
- limiting financial risks for electricity distributor buying (respectively, producer selling) PV power on the electricity market.
- designing optimal rules for the management of micro-grids including at least a PV system and a battery storage.
2. Forecasting Using Geostationary Satellite Images
2.1. Preliminaries: Using Satellite Data to Assess GHI
2.2. Overview of Satellite-Based Intraday Forecast Methods
2.3. Material and Methods
2.3.1. Validation Data
2.3.2. Satellite Data
2.3.3. ARPEGE Outputs
2.3.4. Cloud Index Computation
2.3.5. Cloud Motion Vector Field Computation
- In the first step, a CMV field is produced with a block-matching method based on the minimization of the sum of squared differences (SSD, or Euclidean distance) of pixel values (radiances). For each vector, if the forecast is initiated at time T0, a square of 36 × 36 pixels—called target window—is selected on the image acquired at T0-15 min (image 1). A square of 96× 96 pixels—the search window—is then selected in the image acquired at T0 (image 2) with the same center as the target window of image 1. The target window of image 1 is displaced pixel-by-pixel in all directions, over all possible positions allowed inside the search window of image 2. The relative position of the two windows presenting the minimal SSD is marked as the motion vector tip, and thus defines the CMV. In practice this process is applied to all the predefined positions of a regular grid and produces a CMV vector field between T0-15 min and T0. In this study we do not take into account the height (or level) of the tracked clouds. The resulting CMV fields may be composed of vectors at different levels. When clouds are present in different, overlapping layers, the satellite generally “sees” the clouds of the uppermost layer in the visible channel.
- A second CMV field has to be calculated over the same grid with the previously described procedure between the image acquired at T0-30 min (image 0) and the image acquired at T0-15 min (image 1), in order to detect unrealistic temporal evolution of the tracked cloud or structure during the half-hour period before T0, the forecast time, by comparing both vector fields. In practice, this second CMV field is extracted before the one derived between T0-15 min and T0).
- Suppression of null vectors (corresponding often to cloud free areas).
- Suppression of vectors with a norm exceeding a realistic wind speed.
- Temporal consistency test: each vector calculated between T0-15 min and T0 is compared to its predecessor computed at the same location in the images acquired at T0-30 min and T0-15 min. If the difference in vector direction or the ratio of magnitude (proportional to the wind speed) of both vectors, exceed fixed thresholds, the vector is suppressed.
- Spatial consistency test: each vector is compared with its nearest neighbors. Similarly, if the difference in direction or the magnitude ratio exceeds fixed thresholds, the vector is suppressed.
- When clouds appear or dissipate (inside the area covered by the target window), they are only present on one image of a pair.
- Sometimes two clouds or cloud groups (or more) can be present at different levels inside a target window and can move at a different speeds or in different directions. The derived CMV may correspond to the motion of one of the clouds (generally the largest one, or in some cases the thickest cloud when semi-transparent clouds (cirrus) are also present. Or the CMV may correspond to some “mixture” of the motion of both clouds.
2.3.6. Cloud Index Image Extrapolation and Post-Processing
2.3.7. Forecast GHI Computation
3. Forecast Predictors
- The deterministic variability only dependent on the course of the Sun, directly and entirely quantified by the solar zenith angle θ.
- The stochastic variability of cloud cover, depending on the weather situation at multiple spatio-temporal scales. Thus, its quantification can be made through various parameters influencing cloudiness at the intra-day scale.
3.1. Predictor Solar Zenith Angle
3.2. “Observed” Clear-Sky Index
3.3. Spatial Pattern of Surrounding Clear-Sky Indices Assessed from Satellite
3.4. Weather Regimes of North Atlantic-Europe Domain
- An anticyclone over northern Canada and Greenland and a zonal circulation towards Europe: this regime corresponds to the negative value of the Northern Atlantic Oscillation (NAO) and it is therefore called NAO−.
- An anticyclone in the middle of the Atlantic. This regime is called the Atlantic Ridge.
- An anticyclone over the North Sea: Scandinavian Blocking.
- A depression over Iceland in winter and a pronounced zonal circulation toward Europe in summer. This regime corresponds to the positive phase of the NAO (or NAO+). It is called the Atlantic Low in winter and Zonal in summer.
4. Results
4.1. Evaluation Protocol
4.2. General Results
4.3. Results against Punctual Predictors
4.4. Results against Neighboring Clear-Sky Index Pattern Observed by Satellite
4.5. Results against Synoptic Weather Regimes
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Season | Weather Regime | GHI Mean (Wm−2) | Number of Observations |
---|---|---|---|
Summer | All regimes | 455 | 12,837 |
NAO− | 454 | 3139 | |
Atlantic Ridge | 421 | 3642 | |
Scandinavian Blocking | 490 | 2985 | |
Zonal (NAO+) | 467 | 3071 | |
Winter | All regimes | 176 | 5988 |
NAO− | 183 | 455 | |
Atlantic Ridge | 135 | 1635 | |
Scandinavian Blocking | 215 | 1555 | |
Atlantic low (NAO+) | 178 | 2343 |
Forecast Horizon (min) | Observation Mean Wm−2 | Mean Bias Error W.m−2 (%) | Mean Absolute Error Wm−2 (%) | Root Mean Square Error Wm−2 (%) | Correlation Coefficient | Observation Number |
---|---|---|---|---|---|---|
0 | 364 | −17 (−5) | 57 (16) | 85 (23) | 0.94 | 36,541 |
15 | 364 | −16 (−4) | 58 (16) | 86 (24) | 0.94 | 36,327 |
60 | 382 | −16 (−4) | 70 (18) | 105 (27) | 0.91 | 33,473 |
120 | 408 | −18 (−4) | 87 (21) | 126 (31) | 0.87 | 29,131 |
180 | 425 | −18 (−4) | 99 (23) | 141 (33) | 0.84 | 25,043 |
240 | 431 | −17 (−4) | 107 (25) | 152 (35) | 0.83 | 25,043 |
360 | 420 | −11 (−2) | 115 (27) | 163 (39) | 0.78 | 14,067 |
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Cros, S.; Badosa, J.; Szantaï, A.; Haeffelin, M. Reliability Predictors for Solar Irradiance Satellite-Based Forecast. Energies 2020, 13, 5566. https://doi.org/10.3390/en13215566
Cros S, Badosa J, Szantaï A, Haeffelin M. Reliability Predictors for Solar Irradiance Satellite-Based Forecast. Energies. 2020; 13(21):5566. https://doi.org/10.3390/en13215566
Chicago/Turabian StyleCros, Sylvain, Jordi Badosa, André Szantaï, and Martial Haeffelin. 2020. "Reliability Predictors for Solar Irradiance Satellite-Based Forecast" Energies 13, no. 21: 5566. https://doi.org/10.3390/en13215566