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LORAP: Local Deep Neural Network for Solar Radiation Prediction

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Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications (FDSE 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1925))

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Abstract

Solar radiation directly affects human health and the surrounding environment. Therefore, scientists are paying much attention to this aspect to control the level of radiation. This paper introduces a new model to predict solar radiation using the collected dataset. Our approach focuses on predicting solar radiation frequency with a deep-learning network model. Instead of ideas directly indicating the outcome with one regression model (deep learning or machine learning), we take inspiration from the saying “divide and conquer” to propose a layered learning model. We implement classification models before building local regression models for classes. Our proposal obtains the expected results with \(99\%\) accuracy for the classification and an MAE of 17.8556 for the regression model. In this paper, we also compare our approach with existing models. Two highlights are: (1) our model is better than several approaches, and (2) it forecasts the ability of solar radiation in the next fifteen minutes based on the current information/data.

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Author information

Authors and Affiliations

  1. CICT, Can Tho University, Can Tho, Vietnam

    Thanh-Tri Trang, Thanh Ma & Thanh-Nghi Do

  2. UMI UMMISCO 209, IRD/UPMC, Paris, France

    Thanh-Nghi Do

Authors
  1. Thanh-Tri Trang
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  2. Thanh Ma
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  3. Thanh-Nghi Do
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Correspondence to Thanh Ma .

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Editors and Affiliations

  1. Ho Chi Minh City University of Industry and Trade, Ho Chi Minh City, Vietnam

    Tran Khanh Dang

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. Sungkyunkwan University, Suwon-si, Korea (Republic of)

    Tai M. Chung

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Trang, TT., Ma, T., Do, TN. (2023). LORAP: Local Deep Neural Network for Solar Radiation Prediction. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_26

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  • DOI: https://doi.org/10.1007/978-981-99-8296-7_26

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8295-0

  • Online ISBN: 978-981-99-8296-7

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