AI-based forecasting for optimised solar energy management and smart grid efficiency
Name
AI-based forecasting for optimised solar energy management and smart grid efficiency (5).pdf
Size
2.76 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Bouquet, Pierre
Jackson, Ilya
Nick, Mostafa
Kaboli, Amin
Date Issued
October 16, 2023
Journal
International Journal of Production Research
Publisher
Informa UK Limited
Citation
Pierre Bouquet, Ilya Jackson, Mostafa Nick & Amin Kaboli (2023) AI-based forecasting for optimised solar energy management and smart grid efficiency, International Journal of Production Research.
Version
Final published version
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 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.
Subjects
Industrial and Manufacturing Engineering
Management Science and Operations Research
Strategy and Management
MIT Department
Massachusetts Institute of Technology. Center for Transportation & Logistics
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1080/00207543.2023.2269565
