insight - Machine Learning - # Satellite-derived Solar Irradiance Estimation

Estimating Thailand's Solar Irradiance Using Himawari-8 Satellite Imagery and Deep Learning Models

Q: How can the proposed methodology be extended to provide solar irradiance forecasts at higher temporal resolutions (e.g., 10-minute or 5-minute) to better support grid integration and operational planning of solar PV systems?

To extend the proposed methodology for solar irradiance forecasts at higher temporal resolutions, such as 10-minute or 5-minute intervals, several strategies can be implemented. First, the data acquisition frequency from the Himawari-8 satellite can be leveraged, as it provides cloud imagery every 10 minutes. By utilizing this high-frequency data, the model can be trained to predict GHI based on shorter time intervals, allowing for more granular forecasting. Second, the deep learning models, particularly those like LSTM, Informer, and Transformer, can be adapted to handle shorter input sequences. This involves modifying the input data structure to include more frequent cloud index readings and corresponding meteorological data, thereby enhancing the model's ability to capture rapid changes in solar irradiance due to fluctuating cloud cover. Additionally, incorporating real-time ground measurements from solar PV systems can improve the model's responsiveness to immediate changes in solar conditions. By integrating these data points, the model can adjust its predictions dynamically, providing more accurate forecasts that align with the operational needs of grid integration. Finally, implementing ensemble forecasting techniques that combine predictions from multiple models can enhance reliability. By aggregating outputs from various models trained on different aspects of the data, the overall forecast accuracy can be improved, making it more suitable for operational planning and grid management.

Q: What are the potential limitations or challenges in applying the deep learning models developed in this study to other geographic regions with different climate conditions?

Applying the deep learning models developed in this study to other geographic regions presents several potential limitations and challenges. One significant challenge is the variability in climate conditions, which can affect the accuracy of solar irradiance predictions. Regions with different cloud patterns, humidity levels, and atmospheric compositions may require model retraining or fine-tuning to account for these local characteristics. Moreover, the availability and quality of ground measurement data can vary significantly across regions. In areas with sparse or unreliable ground data, the models may struggle to learn accurate relationships between satellite-derived inputs and actual GHI, leading to suboptimal performance. This is particularly critical in regions where ground stations are limited, as the models rely heavily on high-quality training data for effective learning. Another challenge is the potential differences in the satellite imagery used for training. The Himawari-8 satellite provides specific spectral bands and resolutions that may not be available from other satellites. Consequently, models trained on Himawari-8 data may not generalize well to data from different satellite sources, necessitating the development of new models or adaptations for different satellite systems. Lastly, the computational resources required for real-time processing and forecasting may vary based on the region's infrastructure. Areas with limited computational capabilities may face challenges in deploying complex deep learning models, which could hinder the practical application of the developed methodologies.

Q: How could the integration of additional data sources, such as numerical weather prediction models or sky imagers, further improve the accuracy and reliability of the satellite-derived solar irradiance estimates?

Integrating additional data sources, such as numerical weather prediction (NWP) models and sky imagers, can significantly enhance the accuracy and reliability of satellite-derived solar irradiance estimates. NWP models provide detailed atmospheric data, including temperature, humidity, wind speed, and precipitation forecasts, which can be crucial for understanding the factors influencing solar irradiance. By incorporating NWP data, the models can better account for atmospheric conditions that affect cloud formation and behavior, leading to more accurate GHI predictions. Sky imagers, which capture real-time images of the sky, can provide valuable information about cloud cover and type. This additional layer of data can be used to refine cloud index calculations and improve the model's ability to predict short-term fluctuations in solar irradiance. By analyzing the visual data from sky imagers, the models can learn to recognize specific cloud patterns and their impacts on solar radiation, enhancing the overall forecasting capability. Furthermore, combining these data sources with the existing satellite imagery can create a more comprehensive dataset for training the deep learning models. This multi-faceted approach allows for a richer understanding of the interactions between various atmospheric parameters and solar irradiance, ultimately leading to improved model performance. Additionally, the integration of these data sources can facilitate the development of hybrid models that leverage both physical and data-driven approaches. Such models can utilize the strengths of each method, providing a more robust framework for solar irradiance estimation that is adaptable to varying conditions and capable of delivering reliable forecasts across different geographic regions.

Conceitos essenciais

A methodology that exploits deep learning and tree-based models to accurately estimate global horizontal irradiance (GHI) across Thailand using Himawari-8 satellite cloud data, clear-sky irradiance, and other meteorological features.

Resumo

The paper presents a framework for developing an online platform that provides a Thailand solar irradiance map updated every 30 minutes. The methodology relies on cloud index extracted from Himawari-8 satellite imagery, a locally-tuned Ineichen clear-sky model, and machine learning models including LightGBM, LSTM, Informer, and Transformer.

The authors benchmark these models against the SolCast commercial service using 15-minute ground GHI data from 53 stations over 1.5 years. The results show that the proposed models outperform SolCast, with LightGBM achieving the best performance with a mean absolute error (MAE) of 78.58 W/m^2 and root mean squared error (RMSE) of 118.97 W/m^2.

When the re-analyzed MERRA-2 data is removed as a feature, the Informer model has the winning performance with an MAE of 78.67 W/m^2. The obtained performance aligns with existing literature by considering the climate zone and time granularity of the data.

The paper also describes a computational framework for displaying the entire Thailand solar irradiance map, which covers 93,000 grids, and tests the runtime performance of the deep learning models in the GHI estimation process.

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Estatísticas

"The best model, LightGBM, has an MAE of 78.58 W/m^2 and RMSE of 118.97 W/m^2 for estimating GHI across Thailand."
"When removing the re-analyzed MERRA-2 data as a feature, the Informer model has an MAE of 78.67 W/m^2."

Citações

"The reliability of these [solar irradiance] forecasts directly impacts the planning (pipeline) of solar photovoltaic (PV) systems, ensuring they are integrated efficiently into the power grid, and also influences the operational settings required for various energy applications."
"To accommodate solar energy applications in Thailand in an operational setting, we aim to develop our own solar map that frequently estimates satellite-derived GHI across the country using satellite imagery and local ground measurement."

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Developing a Thailand solar irradiance map using Himawari-8 satellite imageries and deep learning models

by Suwichaya Su... às arxiv.org 09-26-2024

https://arxiv.org/pdf/2409.16320.pdf

Developing a Thailand solar irradiance map using Himawari-8 satellite imageries and deep learning models

Perguntas Mais Profundas

How can the proposed methodology be extended to provide solar irradiance forecasts at higher temporal resolutions (e.g., 10-minute or 5-minute) to better support grid integration and operational planning of solar PV systems?

To extend the proposed methodology for solar irradiance forecasts at higher temporal resolutions, such as 10-minute or 5-minute intervals, several strategies can be implemented. First, the data acquisition frequency from the Himawari-8 satellite can be leveraged, as it provides cloud imagery every 10 minutes. By utilizing this high-frequency data, the model can be trained to predict GHI based on shorter time intervals, allowing for more granular forecasting.
Second, the deep learning models, particularly those like LSTM, Informer, and Transformer, can be adapted to handle shorter input sequences. This involves modifying the input data structure to include more frequent cloud index readings and corresponding meteorological data, thereby enhancing the model's ability to capture rapid changes in solar irradiance due to fluctuating cloud cover.
Additionally, incorporating real-time ground measurements from solar PV systems can improve the model's responsiveness to immediate changes in solar conditions. By integrating these data points, the model can adjust its predictions dynamically, providing more accurate forecasts that align with the operational needs of grid integration.
Finally, implementing ensemble forecasting techniques that combine predictions from multiple models can enhance reliability. By aggregating outputs from various models trained on different aspects of the data, the overall forecast accuracy can be improved, making it more suitable for operational planning and grid management.

What are the potential limitations or challenges in applying the deep learning models developed in this study to other geographic regions with different climate conditions?

Applying the deep learning models developed in this study to other geographic regions presents several potential limitations and challenges. One significant challenge is the variability in climate conditions, which can affect the accuracy of solar irradiance predictions. Regions with different cloud patterns, humidity levels, and atmospheric compositions may require model retraining or fine-tuning to account for these local characteristics.
Moreover, the availability and quality of ground measurement data can vary significantly across regions. In areas with sparse or unreliable ground data, the models may struggle to learn accurate relationships between satellite-derived inputs and actual GHI, leading to suboptimal performance. This is particularly critical in regions where ground stations are limited, as the models rely heavily on high-quality training data for effective learning.
Another challenge is the potential differences in the satellite imagery used for training. The Himawari-8 satellite provides specific spectral bands and resolutions that may not be available from other satellites. Consequently, models trained on Himawari-8 data may not generalize well to data from different satellite sources, necessitating the development of new models or adaptations for different satellite systems.
Lastly, the computational resources required for real-time processing and forecasting may vary based on the region's infrastructure. Areas with limited computational capabilities may face challenges in deploying complex deep learning models, which could hinder the practical application of the developed methodologies.

How could the integration of additional data sources, such as numerical weather prediction models or sky imagers, further improve the accuracy and reliability of the satellite-derived solar irradiance estimates?

Integrating additional data sources, such as numerical weather prediction (NWP) models and sky imagers, can significantly enhance the accuracy and reliability of satellite-derived solar irradiance estimates. NWP models provide detailed atmospheric data, including temperature, humidity, wind speed, and precipitation forecasts, which can be crucial for understanding the factors influencing solar irradiance. By incorporating NWP data, the models can better account for atmospheric conditions that affect cloud formation and behavior, leading to more accurate GHI predictions.
Sky imagers, which capture real-time images of the sky, can provide valuable information about cloud cover and type. This additional layer of data can be used to refine cloud index calculations and improve the model's ability to predict short-term fluctuations in solar irradiance. By analyzing the visual data from sky imagers, the models can learn to recognize specific cloud patterns and their impacts on solar radiation, enhancing the overall forecasting capability.
Furthermore, combining these data sources with the existing satellite imagery can create a more comprehensive dataset for training the deep learning models. This multi-faceted approach allows for a richer understanding of the interactions between various atmospheric parameters and solar irradiance, ultimately leading to improved model performance.
Additionally, the integration of these data sources can facilitate the development of hybrid models that leverage both physical and data-driven approaches. Such models can utilize the strengths of each method, providing a more robust framework for solar irradiance estimation that is adaptable to varying conditions and capable of delivering reliable forecasts across different geographic regions.