Bouquet, PierreJackson, IlyaNick, MostafaKaboli, Amin2024-02-162024-02-162024-02-162023-10-1410.1080/00207543.2023.2269565https://infoscience.epfl.ch/handle/20.500.14299/203867WOS:001083979000001This 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. 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.TechnologyArtificial IntelligenceShortage EconomyEnergy OperationsDeep LearningSmart GridsRenewable Energytext::journal::journal article::research article