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A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation

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Book cover Recent Advances in Technology Research and Education (INTER-ACADEMIA 2018)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 53))

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Abstract

In this paper, we present a Cluster-Based Approach (CBA) that utilizes the support vector machine (SVM) and an artificial neural network (ANN) to estimate and predict the daily horizontal global solar radiation. In the proposed CBA-ANN-SVM approach, we first conduct clustering analysis and divided the global solar radiation data into clusters, according to the calendar months. Our approach aims at maximizing the homogeneity of data within the clusters, and the heterogeneity between the clusters. The proposed CBA-ANN-SVM approach is validated and the precision is compared with ANN and SVM techniques. The mean absolute percentage error (MAPE) for the proposed approach was reported lower than those of ANN and SVM.

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Acknowledgment

This work has partially been sponsored by the Hungarian National Scientific Fund under contract OTKA 129374 and the Research & Development Operational Program for the project “Modernization and Improvement of Technical Infrastructure for Research and Development of J. Selye University in the Fields of Nanotechnology and Intelligent Space”, ITMS 26210120042, co-funded by the European Regional Development Fund. Dr. Mosavi contributed in this research during the tenure of an ERCIM Alain Bensoussan Fellowship Programme. The support and research infrastructure of Institute of Advanced Studies Koszeg, iASK, is acknowledged.

Author information

Authors and Affiliations

  1. Hormozgan Regional Electric Co, Bandarabbas, Iran

    Mehrnoosh Torabi

  2. Institute of Advanced Studies Koszeg, iASK, Kőszeg, Hungary

    Amir Mosavi

  3. Institute of Automation, Kando Kalman, Faculty of Electrical Engineering, Obuda University, Budapest, 1431, Hungary

    Amir Mosavi, Annamaria Varkonyi-Koczy & Vajda Istvan

  4. Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway

    Amir Mosavi & Pinar Ozturk

  5. Department of Mathematics and Informatics, J. Selye University, Komarno, Slovakia

    Annamaria Varkonyi-Koczy

Authors
  1. Mehrnoosh Torabi
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  2. Amir Mosavi
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  3. Pinar Ozturk
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  4. Annamaria Varkonyi-Koczy
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  5. Vajda Istvan
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Corresponding author

Correspondence to Amir Mosavi .

Editor information

Editors and Affiliations

  1. Department of Physics, Faculty of Mathematics and Natural Sciences, Kaunas University of Technology, Kaunas, Lithuania

    Giedrius Laukaitis

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Torabi, M., Mosavi, A., Ozturk, P., Varkonyi-Koczy, A., Istvan, V. (2019). A Hybrid Machine Learning Approach for Daily Prediction of Solar Radiation. In: Laukaitis, G. (eds) Recent Advances in Technology Research and Education. INTER-ACADEMIA 2018. Lecture Notes in Networks and Systems, vol 53. Springer, Cham. https://doi.org/10.1007/978-3-319-99834-3_35

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