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

Carrière, Thomas; Silva, Rodrigo Amaro e; Zhuang, Fuqiang; Saint‐Drenan, Yves‐Marie; Blanc, Philippe

doi:10.3390/en14164951

Cited by 11 publications

(5 citation statements)

References 52 publications

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“…Probabilistic solar irradiance forecasting with DL offers a richer modelling framework than single irradiance level prediction (Carrière et al [6]). In particular, uncertainty quantification is valuable for many applications (hybrid power plant, network balance, electricity trading, etc.).…”

Section: Uncertainty Quantificationmentioning

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: Uncertainty Quantificationmentioning

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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“…This paper's probabilistic forecasts account for the fact that approaching clouds are not yet visible to the TSI (on an hourly timescale). Combining probabilistic forecasts with sky images is pertinent for hourly predictions, which are typically made using satellite images [29,30].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting

et al. 2023

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For grid stability, operation, and planning, solar irradiance forecasting is crucial. In this paper, we provide a method for predicting the Global Horizontal Irradiance (GHI) mean values one hour in advance. Sky images are utilized for training the various forecasting models along with measured meteorological data in order to account for the short-term variability of solar irradiance, which is mostly caused by the presence of clouds in the sky. Additionally, deep learning models like the multilayer perceptron (MLP), convolutional neural networks (CNN), long short-term memory (LSTM), or their hybridized forms are widely used for deterministic solar irradiance forecasting. The implementation of probabilistic solar irradiance forecasting, which is gaining prominence in grid management since it offers information on the likelihood of different outcomes, is another task we carry out using quantile regression. The novelty of this paper lies in the combination of a hybrid deep learning model (CNN-LSTM) with quantile regression for the computation of prediction intervals at different confidence levels. The training of the different machine learning algorithms is performed over a year’s worth of sky images and meteorological data from the years 2019 to 2020. The data were measured at the University of French Polynesia (17.5770° S, 149.6092° W), on the island of Tahiti, which has a tropical climate. Overall, the hybrid model (CNN-LSTM) is the best performing and most accurate in terms of deterministic and probabilistic metrics. In addition, it was found that the CNN, LSTM, and ANN show good results against persistence.

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“…However, owing to the untidy and unpredictable characteristics of the climate conditions, the power production from the SPV system always exposes solid insecurities through intermittency, volatility, and uncertainty [3]. These ambiguities may damage However, owing to the untidy and unpredictable characteristics of the climate conditions, the power production from the SPV system always exposes solid insecurities through intermittency, volatility, and uncertainty [3]. These ambiguities may damage the instantaneous control performance, diminish system economics, and hence stress the electrical power and utility system [4].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

et al. 2022

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Variability in solar irradiance has an impact on the stability of solar systems and the grid’s safety. With the decreasing cost of solar panels and recent advancements in energy conversion technology, precise solar energy forecasting is critical for energy system integration. Despite extensive research, there is still potential for advancement of solar irradiance prediction accuracy, especially global horizontal irradiance. Global Horizontal Irradiance (GHI) (unit: KWh/m2) and the Plane Of Array (POA) irradiance (unit: W/m2) were used as the forecasting objectives in this research, and a hybrid short-term solar irradiance prediction model called modified multi-step Convolutional Neural Network (CNN)-stacked Long-Short-Term-Memory network (LSTM) with drop-out was proposed. The real solar data from Sweihan Photovoltaic Independent Power Project in Abu Dhabi, UAE is preprocessed, and features were extracted using modified CNN layers. The output result from CNN is used to predict the targets using a stacked LSTM network and the efficiency is proved by comparing statistical performance measures in terms of Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), and R2 scores, with other contemporary machine learning and deep-learning-based models. The proposed model offered the best RMSE and R2 values of 0.36 and 0.98 for solar irradiance prediction and 61.24 with R2 0.96 for POA prediction, which also showed better performance as compared to the published works in the literature.

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

Cited by 11 publications

References 52 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

Hybrid Deep Learning Model for Mean Hourly Irradiance Probabilistic Forecasting

Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique

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