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Solar Energy Forecasting Using Machine Learning and Deep Learning Techniques

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Abstract

Renewable energy sources are present copiously in the nature and are good for environmental conservation as they restore themselves and thus have considerable potential in the near future. It is hence important to concentrate on the forecast of these energy sources in order to make effective use of them as soon as possible. This paper is focused primarily on solar energy. There are many approaches that could be applied for the prediction of global solar radiation (GSR). In the field of artificial intelligence (AI), the forecasting of solar resources has moved from conventional mathematical approaches to the use of intelligent techniques. The extent to which data based decisions are made for planning such as judicious and functional for the solar energy sector has been increased to a large extent by this giant step. In modelling challenging and unpredictable connections in between a set of input data and output data along with specific patterns that occur between datasets, AI techniques have demonstrated increasing reliability. In this regard, purpose of this paper is to provide a synopsis of solar energy forecasting methods utilizing machine learning and deep learning approaches to the best of our understanding.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Park College of Engineering & Technology, Coimbatore, Tamil Nadu, India

    T. Rajasundrapandiyanleebanon & Sakthivel Murugan

  2. Department of Mechanical Engineering, Park College of Engineering & Technology, Coimbatore, Tamil Nadu, India

    K. Kumaresan

  3. Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India

    M. S. P. Subathra

  4. Accenture Services Pvt Ltd, Pune, India

    Mahima Sivakumar

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  1. T. Rajasundrapandiyanleebanon
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Rajasundrapandiyanleebanon, T., Kumaresan, K., Murugan, S. et al. Solar Energy Forecasting Using Machine Learning and Deep Learning Techniques. Arch Computat Methods Eng 30, 3059–3079 (2023). https://doi.org/10.1007/s11831-023-09893-1

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