Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery

Rodríguez-Benítez, Francisco J.; López-Cuesta, Miguel; Arbizu‐Barrena, Clara; Fernández-León, María M.; Pamos-Ureña, Miguel Á.; Tovar‐Pescador, J.; Santos-Alamillos, Francisco J.; Pozo-Vázquez, D.

doi:10.1016/j.apenergy.2021.116838

Cited by 40 publications

(25 citation statements)

References 48 publications

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“…), some of the methods discussed above have been experimented with in both contexts (optical flow, block matching, image segmentation using neural networks, etc.). Some articles have tried to compare both forecasting approaches (from sky or satellite images) independently in different sky conditions and different forecast horizons to evaluate the conditions in which one prevails over the other (Alonso and Batlles [1], Rodríguez-Benítez et al [47]). However, to the best of our knowledge, the only work proposing an actual data fusion approach was done by Vallance [55] in his doctorate dissertation.…”

Section: Irradiance Forecasting From Sky and Satellite Imagesmentioning

confidence: 99%

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

Quentin¹,

Arbod²,

Lasenby³

2022

Preprint

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Integration of intermittent renewable energy sources into electric grids in large proportions is challenging. A wellestablished approach aimed at addressing this difficulty involves the anticipation of the upcoming energy supply variability to adapt the response of the grid. In solar energy, short-term changes in electricity production caused by occluding clouds can be predicted at different time scales from all-sky cameras (up to 30-min ahead) and satellite observations (up to 6h ahead). In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting. Both deterministic and probabilistic predictions are evaluated in different weather conditions (clear-sky, cloudy, overcast) and with different input configurations (sky images, satellite observations and/or past irradiance values). Our results show that the hybrid model benefits predictions in clear-sky conditions and improves longer-term forecasting. This study lays the groundwork for future novel approaches of combining sky images and satellite observations in a single learning framework to advance solar nowcasting.

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Section: Irradiance Forecasting From Sky and Satellite Imagesmentioning

confidence: 99%

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

Quentin¹,

Arbod²,

Lasenby³

2022

Preprint

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“…Bayesian information criterion (BIC) was used to determine the input dimension of the forecasting model using DNI clear-sky index; that is, the DNI at moment t was related to the DNI at the previous n moments [32]. The BIC was used to determine the order of the DNI clear sky index sequence after the first-order difference, and the obtained BIC thermal diagram is shown in Figure 4.…”

Section: Input Dimensionmentioning

confidence: 99%

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Zhu

Guo

et al. 2021

Energies

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Photovoltaic power generation is highly valued and has developed rapidly throughout the world. However, the fluctuation of solar irradiance affects the stability of the photovoltaic power system and endangers the safety of the power grid. Therefore, ultra-short-term solar irradiance predictions are widely used to provide decision support for power dispatching systems. Although a great deal of research has been done, there is still room for improvement regarding the prediction accuracy of solar irradiance including global horizontal irradiance, direct normal irradiance and diffuse irradiance. This study took the direct normal irradiance (DNI) as prediction target and proposed a Siamese convolutional neural network-long short-term memory (SCNN-LSTM) model to predict the inter-hour DNI by combining the time-dependent spatial features of total sky images and historical meteorological observations. First, the features of total sky images were automatically extracted using a Siamese CNN to describe the cloud information. Next, the image features and meteorological observations were fused and then predicted the DNI in 10-min ahead using an LSTM. To verify the validity of the proposed SCNN-LSTM model, several experiments were carried out using two-year historical observation data provided by the National Renewable Energy Laboratory (NREL). The results show that the proposed method achieved nRMSE of 23.47% and forecast skill of 24.51% for the whole year of 2014, and it also did better than some published methods especially under clear sky and rainy days.

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“…Nouri et al (2018) used four ASIs to derive height information and even a 3dimensional cloud representation for irradiance nowcasting. Three ASIs were used by Rodríguez-Benítez et al (2021) for three independent DNI nowcasts which are finally averaged into a mean DNI nowcast.…”

Section: Introductionmentioning

confidence: 99%

Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

Gregor

Zinner

Jakub

et al. 2023

Preprint

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Abstract. This work introduces the novel short-term nowcasting model MACIN, which predicts direct normal irradiance (DNI) for solar energy applications based on hemispheric sky images from two all-sky imagers (ASI). With a synthetic setup based on simulated cloud scenes, the model and its components were validated in depth. We trained a convolutional neural network to identify clouds in ASI images and derive their height and motion using sparse matching. In contrast to other studies, all derived cloud information from both ASIs and multiple timesteps are combined into an optimal model state using techniques from data assimilation. This state is advected to predict future cloud positions and compute DNI for lead times up to 20 minutes. For the cloudmasks derived from the ASI images we found a pixel accuracy of 94.66 % compared to the references available in the synthetic setup. The relative error of derived cloud base heights is 4 % and cloud motion error is in the range of 0.1 ms−1. For the DNI nowcasts, we found an improvement over persistence for lead times larger than one minute. Using the synthetic setup, we computed a DNI reference for a point and also an area of 500m × 500 m. Errors for area nowcasts as required, e.g., for photovoltaics plants, are smaller compared to errors for point nowcasts. Overall, the novel ASI nowcasting model and its components proved to work within the synthetic setup.

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Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery

Cited by 40 publications

References 48 publications

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions

Solar Radiation Prediction Based on Convolution Neural Network and Long Short-Term Memory

Validation of a camera-based intra-hour irradiance nowcasting model using synthetic cloud data

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