Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics

Verbois, Hadrien; Blanc, Philippe; Huva, Robert; Saint‐Drenan, Yves‐Marie; Rusydi, Andrivo; Thiéry, Alexandre H.

doi:10.1016/j.rser.2019.109471

Cited by 12 publications

(9 citation statements)

References 48 publications

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“…In the end, forecasts would simply be the average of the satellite pictures, and all information about the cloud motions would be lost. A similar trade-off has been reported for deterministic settings when performance assessment relies in quadratic error measures (such as CRPS) [45]. Such metrics tend to favor smooth forecasts as to avoid large errors.…”

Section: Identification Of the Candidate Pixelssupporting

confidence: 64%

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Carrière¹,

Silva

Zhuang

et al. 2021

Energies

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Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

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Section: Identification Of the Candidate Pixelssupporting

confidence: 64%

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Carrière¹,

Silva

Zhuang

et al. 2021

Energies

Self Cite

View full text Add to dashboard Cite

show abstract

“…Refs. [90] , [91] , [92] proved that stepwise regression analysis (SRA) [93] is more efficient for variable selection than alternative methods. So, we adopt SRA to select variables.…”

Section: Application Using Real Datamentioning

confidence: 99%

“…For a given modeling problem, useful variables must be separated from the unnecessary ones. SRA [93] defines predictor variables that most accurately characterize the variable to be predicted [112] . For this, we start by regressing the dependent variable on a single independent variable and set the significance level and values of

(F-to-remove) and

(F-to-enter) [112] ; F being the Fisher statistic.…”

Section: Stepwise Regression Analysis (Sra)mentioning

confidence: 99%

Forecasting petroleum products consumption in Cameroon's household sector using a sequential GMC(1,n) model optimized by genetic algorithms

Sapnken¹,

Ahmat²,

Boukar³

et al. 2022

Heliyon

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“…Figure 13 presents a Taylor diagram [47] with the overall CMF error for the PRS and the FRB COT forecasts as a function of the correlation coefficient, normalized RMSE and normalized standard deviation under all conditions and during TRN conditions. The use of this diagram was highlighted by numerous forecasting evaluation studies dealing with point-based comparisons as well as with image-based validation [48,49]. A note for this kind of analysis is that the PRS in some cases, like going from cloudy to clear sky conditions, presents better results than starting from clear sky to cloudy sky because returning to clear sky or CMF equals to one means that we go back to the optimum operating conditions for PRS, which are the clear sky conditions.…”

Section: Vs Prsmentioning

confidence: 99%

Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

et al. 2020

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This study focuses on the use of cloud motion vectors (CMV) and fast radiative transfer models (FRTM) in the prospect of forecasting downwelling surface solar irradiation (DSSI). Using near-real-time cloud optical thickness (COT) data derived from multispectral images from the spinning enhanced visible and infrared imager (SEVIRI) onboard the Meteosat second generation (MSG) satellite, we introduce a novel short-term forecasting system (3 h ahead) that is capable of calculating solar energy in large-scale (1.5 million-pixel area covering Europe and North Africa) and in high spatial (5 km over nadir) and temporal resolution (15 min intervals). For the operational implementation of such a big data computing architecture (20 million simulations in less than a minute), we exploit a synergy of high-performance computing and deterministic image processing technologies (dense optical flow estimation). The impact of clouds forecasting uncertainty on DSSI was quantified in terms of cloud modification factor (CMF), for all-sky and clear sky conditions, for more generalized results. The forecast accuracy was evaluated against the real COT and CMF images under different cloud movement patterns, and the correlation was found to range from 0.9 to 0.5 for 15 min and 3 h ahead, respectively. The CMV forecast variability revealed an overall DSSI uncertainty in the range 18–34% under consecutive alternations of cloud presence, highlighting the ability of the proposed system to follow the cloud movement in opposition to the baseline persistent forecasting, which considers the effects of topocentric sun path on DSSI but keeps the clouds in “fixed” positions, and which presented an overall uncertainty of 30–43%. The proposed system aims to support the distributed solar plant energy production management, as well as electricity handling entities and smart grid operations.

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Beyond quadratic error: Case-study of a multiple criteria approach to the performance assessment of numerical forecasts of solar irradiance in the tropics

Cited by 12 publications

References 48 publications

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Forecasting petroleum products consumption in Cameroon's household sector using a sequential GMC(1,n) model optimized by genetic algorithms

Short-Term Forecasting of Large-Scale Clouds Impact on Downwelling Surface Solar Irradiation

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