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Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data

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Abstract

Solar Irradiance depicts the light energy produced by the Sun that hits the Earth. This energy is important for renewable energy generation and is intrinsically fluctuating. Forecasting solar irradiance is crucial for efficient solar energy generation and management. Work in the literature focused on the short-term prediction of solar irradiance, using meteorological data to forecast the irradiance for the next hours, days, or weeks. Facing climate change and the continuous increase in greenhouse gas emissions, particularly from the use of fossil fuels, the reliance on renewable energy sources, such as solar energy, is expanding. Consequently, governments and practitioners are calling for efficient long-term energy generation plans, which could enable 100% renewable-based electricity systems to match energy demand. In this paper, we aim to perform the long-term prediction of daily solar irradiance, by leveraging the downscaled climate simulations of Global Circulation Models (GCMs). We propose a novel Bayesian deep learning framework, named DeepSI (denoting Deep Solar Irradiance), that employs bidirectional long short-term memory autoencoders, prefixed to a transformer, with an uncertainty quantification component based on the Monte Carlo dropout sampling technique. We use DeepSI to predict daily solar irradiance for three different locations within the United States. These locations include the Solar Star power station in California, Medford in New Jersey, and Farmers Branch in Texas. Experimental results showcase the suitability of DeepSI for predicting daily solar irradiance from the simulated climate data, its superiority over related machine learning methods, and its ability to reproduce the daily variability. We further use DeepSI with future climate simulations to produce long-term projections of daily solar irradiance, up to year 2099.

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Funding

Funding for this study was provided by the Bridge Resource Program (BRP) from the New Jersey Department of Transportation.

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Authors and Affiliations

  1. Department of Computer Science, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA

    Firas Gerges & Jason T. L. Wang

  2. Center for Natural Resources, Department of Civil and Environmental Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA

    Michel C. Boufadel

  3. Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, 08544, USA

    Elie Bou-Zeid

  4. Department of Civil and Environmental Engineering, Rutgers University – New Brunswick, Piscataway, NJ, 08854, USA

    Hani Nassif

Authors
  1. Firas Gerges
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  2. Michel C. Boufadel
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  3. Elie Bou-Zeid
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  4. Hani Nassif
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  5. Jason T. L. Wang
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Contributions

FG wrote the paper, gathered the data, wrote the machine learning scripts and conducted the experimentations. BF, HN and EBZ provided data and results interpretation and contributed to the development of the predictive model through environmental domain expertise. JW supervised the research, interpreted the data, contributed to the writing and to the development of predictive models.

Corresponding author

Correspondence to Firas Gerges.

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Gerges, F., Boufadel, M.C., Bou-Zeid, E. et al. Long-term prediction of daily solar irradiance using Bayesian deep learning and climate simulation data. Knowl Inf Syst 66, 613–633 (2024). https://doi.org/10.1007/s10115-023-01955-x

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