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AI-based forecasting for optimised solar energy management and smart grid efficiency

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  • Pierre Bouquet
  • Ilya Jackson
  • Mostafa Nick
  • Amin Kaboli

Abstract

This paper considers two pertinent research inquiries: ‘Can an AI-based predictive framework be utilised for the optimisation of solar energy management?’ and ‘What are the ways in which the AI-based predictive framework can be integrated within the Smart Grid infrastructure to improve grid reliability and efficiency?’ The study deploys a Deep Learning model based on Long Short-Term Memory techniques, leading to refined accuracy in solar electricity generation forecasts. Such an AI-supported methodology aids power grid operators in comprehensive planning, thereby ensuring a robust electricity supply. The effectiveness of this framework is tested using performance metrics such as MAE, RMSE, nMAE, nRMSE, and $ R^2 $ R2. A persistent model is utilised as a reference for comparison. Despite a slight decrease in predictive precision with the expansion of the forecast horizon, the proposed AI-based framework consistently surpasses the persistent model, particularly for horizons beyond two hours. Therefore, this research underscores the potential of AI-based prediction in fostering efficient solar energy management and enhancing Smart Grid reliability and efficiency.

Suggested Citation

  • Pierre Bouquet & Ilya Jackson & Mostafa Nick & Amin Kaboli, 2024. "AI-based forecasting for optimised solar energy management and smart grid efficiency," International Journal of Production Research, Taylor & Francis Journals, vol. 62(13), pages 4623-4644, July.
  • Handle: RePEc:taf:tprsxx:v:62:y:2024:i:13:p:4623-4644
    DOI: 10.1080/00207543.2023.2269565
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