Posted in | News | Solar Energy

Solar Power Generation Forecasting Using Deep Learning

In a recent article published in the journal Sustainability, researchers investigated a new deep learning (DL) approach for forecasting solar power generation (SPG) across multiple sites. They aimed to develop a scalable and accurate SPG forecasting model that could be applied to various locations using a single common model, addressing the limitations of traditional site-specific models.​​​​​​​

Image Credit: leolintang/Shutterstock.com

​​​​​​​

 

Background

In recent years, renewable power generation, particularly solar power, has witnessed remarkable growth due to its potential to address environmental concerns and its increasing economic viability. However, integrating solar power into the energy grid presents unique challenges because its variability depends on factors like sunlight intensity and duration. Therefore, accurate SPG forecasting is crucial for ensuring grid stability and maximizing solar energy utilization efficiency.

Previous work in SPG forecasting has primarily focused on developing site-specific models. These models require collecting and employing location-specific input data, such as training data and weather conditions, to produce forecasts for an individual site. This approach limits scalability and efficiency when extending forecasting capabilities across multiple sites.

About the Research

In this paper, the authors developed a scalable and accurate SPG forecasting model that can be applied across multiple sites using a single model. They introduced a novel DL-based model that leverages common meteorological elements, such as humidity, temperature, and cloud cover, to extract site-specific features and improve forecasting accuracy.

The proposed model consists of two subsystems: a feature encoder and a regressor. The feature encoder uses convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to extract features from 24 hours of weather data, including solar elevation and azimuth angle. The regressor, a multilayer perceptron (MLP), interprets these encoded features to predict solar power output.

To address the variability of different sites, the study integrated a classifier module within the Encoder system. This classifier helps the encoder to implicitly understand the types of weather data, embedding local site characteristics into the model. This approach enhances the model's robustness and adaptability, enabling reliable forecasts for previously unknown sites by leveraging feature similarities to infer local environmental conditions.

Furthermore, the researchers evaluated the performance of their proposed system using SPG data from seven sites across the Republic of Korea. They compared the performance of a site-specific model, which was trained and tested separately for each site, with a common model, which was trained on data from multiple sites.

Research Findings

The outcomes showed that the site-specific model achieved a mean absolute error (MAE) of 3.43. This significantly surpassed the regulatory requirement of an 8% MAE threshold for participation in the Republic of Korea's renewable energy generation forecasting system. However, the common model without a classifier experienced a drop in prediction accuracy for unknown sites.

Including the classifier module in the common model led to a 3-6% improvement in performance. This demonstrated its effectiveness in using site-specific information to enhance forecasting accuracy across new and diverse locations. The classifier module also reduced the mean squared error (MSE) and root mean squared error (RMSE). This helped minimize larger errors and maintain robust forecasting performance across various sites.

Furthermore, the authors investigated the effectiveness of transfer learning (TL) by retraining the common model with a small subset of site-specific data. The TL scenario improved prediction accuracy across all sites, especially for those with unique meteorological conditions. Including the classifier module in the TL scenario further boosted performance. This emphasized its crucial role in using site-specific information to enhance the model's adaptability and generalization capabilities.

Applications

The presented model has significant implications for the renewable energy sector, especially in improving the operational efficiency of solar power systems. By providing accurate SPG forecasts across multiple sites, the model aids in better grid management and planning, making it easier to integrate solar energy into power grids. This leads to improved stability and efficiency in energy systems, supporting the global shift towards sustainable energy sources. Additionally, the model's scalability and adaptability make it valuable for expanding solar power infrastructure in various geographical regions.

Conclusion

In summary, the DL-based novel approach was effective for forecasting solar power across multiple sites. The paper highlighted the model's robustness and its potential to support the integration of renewable energy into power grids.

Future work should focus on determining the best combination of sites for configuring a common model, exploring hybrid models that combine the strengths of common and site-specific models, and incorporating seasonal variations to enhance accuracy and reliability across different climates. These efforts could enhance the effectiveness and scalability of solar power forecasting models, thereby advancing the integration of renewable energy and ensuring grid stability.

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Source:
  • Jang, S.Y.; Oh, B.T.; Oh, E. A Deep Learning-Based Solar Power Generation Forecasting Method Applicable to Multiple Sites. Sustainability 2024, 16, 5240. DOI: 10.3390/su16125240, https://www.mdpi.com/2071-1050/16/12/5240

Muhammad Osama

Written by

Muhammad Osama

Muhammad Osama is a full-time data analytics consultant and freelance technical writer based in Delhi, India. He specializes in transforming complex technical concepts into accessible content. He has a Bachelor of Technology in Mechanical Engineering with specialization in AI & Robotics from Galgotias University, India, and he has extensive experience in technical content writing, data science and analytics, and artificial intelligence.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Osama, Muhammad. (2024, June 28). Solar Power Generation Forecasting Using Deep Learning. AZoCleantech. Retrieved on October 30, 2024 from https://www.azocleantech.com/news.aspx?newsID=34977.

  • MLA

    Osama, Muhammad. "Solar Power Generation Forecasting Using Deep Learning". AZoCleantech. 30 October 2024. <https://www.azocleantech.com/news.aspx?newsID=34977>.

  • Chicago

    Osama, Muhammad. "Solar Power Generation Forecasting Using Deep Learning". AZoCleantech. https://www.azocleantech.com/news.aspx?newsID=34977. (accessed October 30, 2024).

  • Harvard

    Osama, Muhammad. 2024. Solar Power Generation Forecasting Using Deep Learning. AZoCleantech, viewed 30 October 2024, https://www.azocleantech.com/news.aspx?newsID=34977.

Tell Us What You Think

Do you have a review, update or anything you would like to add to this news story?

Leave your feedback
Your comment type
Submit

While we only use edited and approved content for Azthena answers, it may on occasions provide incorrect responses. Please confirm any data provided with the related suppliers or authors. We do not provide medical advice, if you search for medical information you must always consult a medical professional before acting on any information provided.

Your questions, but not your email details will be shared with OpenAI and retained for 30 days in accordance with their privacy principles.

Please do not ask questions that use sensitive or confidential information.

Read the full Terms & Conditions.