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Data Resource Library for Renewable Energy Prediction/Forecasting

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Renewable Power for Sustainable Growth (ICRP 2023)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 1086))

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Abstract

The objective of this chapter is to develop a data resource library for renewable energy forecasting/prediction using the whole world’s available dataset of the solar and wind domains. A large volume of dataset information and resource files have been collected from Asia, Africa, Latin America, Oceania, and North America regions. Data library for 214 different locations in the world (48 locations of Asia region, 54 locations of Africa region, 44 locations of European region, 33 locations of Latin America region, 14 locations of Oceania region, and 21 locations of North America region) has been prepared. Moreover, 16 data resource libraries are included, which are applicable to the whole world’s locations. These generalized 16 data resource libraries are very useful to collect renewable energy data for those locations where the installation of a metrological station is not feasible.

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Acknowledgements

This study was supported by the Universiti Teknologi Malaysia—“Development of Adaptive and Predictive ACMV/HVAC Health Monitoring System Using IoT, Advanced FDD, and Weather Forecast Algorithms” (Q.J130000.3823.31J06).

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Authors and Affiliations

  1. Department of Electrical Power Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM), Skudai, Malaysia

    Subeyr Bashir Ahmed, Hasmat Malik, Shahrin Md Ayob, Nik Rumzi Nik Idris & Awang Jusoh

  2. Ingenium Research Group, Department, Universidad Castilla-La Mancha, 13071, Ciudad Real, Spain

    Fausto Pedro García Márquez

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  1. Subeyr Bashir Ahmed
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  2. Hasmat Malik
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  3. Shahrin Md Ayob
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  4. Nik Rumzi Nik Idris
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  6. Fausto Pedro García Márquez
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Correspondence to Hasmat Malik .

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Editors and Affiliations

  1. Department of Electrical Power Engineering, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia

    Hasmat Malik

  2. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    Sukumar Mishra

  3. Department of Electrical Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, India

    Y. R. Sood

  4. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Atif Iqbal

  5. Advanced Industrial Science and Technology (FREA), Fukushima Renewable Energy Institute, Koriyama, Japan

    Taha Selim Ustun

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Ahmed, S.B., Malik, H., Ayob, S.M., Idris, N.R.N., Jusoh, A., Márquez, F.P.G. (2024). Data Resource Library for Renewable Energy Prediction/Forecasting. In: Malik, H., Mishra, S., Sood, Y.R., Iqbal, A., Ustun, T.S. (eds) Renewable Power for Sustainable Growth. ICRP 2023. Lecture Notes in Electrical Engineering, vol 1086. Springer, Singapore. https://doi.org/10.1007/978-981-99-6749-0_7

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