A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos

Siddiqui, Talha Ahmad; Bharadwaj, Samarth; Kalyanaraman, Shivkumar

doi:10.1109/wacv.2019.00234

Cited by 36 publications

(32 citation statements)

References 20 publications

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“…Convolutional Neural Network (CNN) models have been shown to recognise specific cloud patterns and adjust their prediction accordingly [47,30]. Regarding the more challenging task of predicting future solar flux or solar energy production, numerous DL architectures have been shown to reach high quantitative performances: CNN [41,9], CNN + Long short Term Memory (LSTM) networks [50,10,30,47,38,29], 3D-CNN [51,29], implicit layers [24], Convolutional LSTM [21,29].…”

Section: Related Workmentioning

confidence: 99%

“…Contrarily to previous architectures [41,50,51,38,24] which predict only a single irradiance value at a given time horizon, we recursively predict future states that are then regressed to future irradiance values (Figure 2). Predicting a sequence of future values instead of a single value allows our model to learn a representation that can detect rapid changes in solar flux, due to cloud occlusion for instance.…”

Section: Model Architecturementioning

confidence: 99%

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ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Paletta,

Hu,

Arbod

et al. 2021

Preprint

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Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency. A promising approach to forecast the temporal variability of solar irradiance resulting from the cloud cover dynamics, is based on the analysis of sequences of ground-taken sky images. Despite encouraging results, a recurrent limitation of current Deep Learning approaches lies in the ubiquitous tendency of reacting to past observations rather than actively anticipating future events. This leads to a systematic temporal lag and little ability to predict sudden events. To address this challenge, we introduce ECLIPSE, a spatiotemporal neural network architecture that models cloud motion from sky images to predict both future segmented images and corresponding irradiance levels. We show that ECLIPSE anticipates critical events and considerably reduces temporal delay while generating visually realistic futures.

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Section: Related Workmentioning

confidence: 99%

Section: Model Architecturementioning

confidence: 99%

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Paletta,

Hu,

Arbod

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The deep RBFNN models outperformed the conventional SVM and conventional feed-forward networks [40]. Sky cameras were utilized to generate a dataset, and a deep learning-based forecasting methodology was investigated [41]. The developed model reported a reduced mean absolute percentage error (MAPE) value compared to that of other conventional models.…”

Section: Introductionmentioning

confidence: 99%

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Jayalakshmi

Shankar²,

Subramaniam

et al. 2021

Energies

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Solar irradiance forecasting is an inevitable and most significant process in grid-connected photovoltaic systems. Solar power is highly non-linear, and thus to manage the grid operation efficiently, with irradiance forecasting for various timescales, such as an hour ahead, a day ahead, and a week ahead, strategies are developed and analysed in this article. However, the single time scale model can perform better for that specific time scale but cannot be employed for other time scale forecasting. Moreover, the data consideration for single time scale forecasting is limited. In this work, a multi-time scale model for solar irradiance forecasting is proposed based on the multi-task learning algorithm. An effective resource sharing scheme between each task is presented. The proposed multi-task learning algorithm is implemented with a long short-term memory (LSTM) neural network model and the performance is investigated for various time scale forecasting. The hyperparameter estimation of the proposed LSTM model is made by a hybrid chicken swarm optimizer based on combining the best features of both the chicken swarm optimization algorithm (CSO) and grey wolf optimization (GWO) algorithm. The proposed model is validated, comparing existing methodologies for single timescale forecasting, and the proposed strategy demonstrated highly consistent performance for all time scale forecasting with improved metric results.

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“…In addition, a similar method has been used as an important step for predicting GHI one hour in advance with one-minute intervals [26]. Other advanced and sophisticated techniques, like convolutional neural networks (CNNs), have been developed and applied in recent years to forecast solar irradiance [27] by offering significant advantages for large image datasets [28], evaluating the non-linearity and other more complex relationships [29].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

2021

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A novel high-resolution method for forecasting cloud motion from all-sky images using deep learning is presented. A convolutional neural network (CNN) was created and trained with more than two years of all-sky images, recorded by a hemispheric sky imager (HSI) at the Institute of Meteorology and Climatology (IMUK) of the Leibniz Universität Hannover, Hannover, Germany. Using the haze indexpostprocessing algorithm, cloud characteristics were found, and the deformation vector of each cloud was performed and used as ground truth. The CNN training process was built to predict cloud motion up to 10 min ahead, in a sequence of HSI images, tracking clouds frame by frame. The first two simulated minutes show a strong similarity between simulated and measured cloud motion, which allows photovoltaic (PV) companies to make accurate horizon time predictions and better marketing decisions for primary and secondary control reserves. This cloud motion algorithm principally targets global irradiance predictions as an application for electrical engineering and in PV output predictions. Comparisons between the results of the predicted region of interest of a cloud by the proposed method and real cloud position show a mean Sørensen–Dice similarity coefficient (SD) of 94 ± 2.6% (mean ± standard deviation) for the first minute, outperforming the persistence model (89 ± 3.8%). As the forecast time window increased the index decreased to 44.4 ± 12.3% for the CNN and 37.8 ± 16.4% for the persistence model for 10 min ahead forecast. In addition, up to 10 min global horizontal irradiance was also derived using a feed-forward artificial neural network technique for each CNN forecasted image. Therefore, the new algorithm presented here increases the SD approximately 15% compared to the reference persistence model.

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A Deep Learning Approach to Solar-Irradiance Forecasting in Sky-Videos

Cited by 36 publications

References 20 publications

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy

Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting

Convolutional Neural Network for High-Resolution Cloud Motion Prediction from Hemispheric Sky Images

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