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

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 $R^2$. 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.

Keywords:

• Artificial intelligence
• shortage economy
• energy operations
• deep learning
• smart grids
• renewable energy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The authors confirm that the data supporting the findings of this study are available. The experiments were conducted in a reproducible manner, and the source code, as well as data are available on the GitHub repository https://github.com/Pierre-Bouquet/LSTM_GHI_Forecasting.

Notes

1 https://github.com/Pierre-Bouquet/LSTM_GHI_Forecasting.

2 https://github.com/DESL-EPFL/DESL-Photovoltaic-timeseries.

Additional information

Notes on contributors

Pierre Bouquet

Pierre Bouquet is a first-year Ph.D. student at the Massachusetts Institute of Technology (MIT), under the mentorship of Prof. Yossi Sheffi. He received both his bachelor's degree and an M.Sc. in Mechanical Engineering from the Swiss Federal Institute of Technology in Lausanne. During his M.Sc., he specialised in Automation Systems and also pursued a minor in Data Science. Pierre is deeply interested in the fields of optimisation, machine learning, and data science, with a particular focus on supply chain and operations optimisation. For inquiries or collaborations, Pierre can be contacted at his academic email: [email protected] or via LinkedIn.

Ilya Jackson

Dr. Ilya Jackson is a Postdoctoral Associate at MIT Center for Transportation & Logistics. He earned his Ph.D. in Civil Engineering and Transportation from the Transport and Telecommunication Institute, where he spent one year as an assistant professor shortly after that. The main ideas of his Ph.D. thesis have been summarised in the paper 'Neuroevolutionary approach to metamodel-based optimisation in production and logistics', which received the Young Researcher Award in 2020. Dr. Ilya Jackson currently focuses on Machine Learning and AI for Supply Chain Management.

Mostafa Nick

Mostafa Nick received the Ph.D. degree in electrical engineering from EPFL, Lausanne, Switzerland, in 2016. From 2016 to 2017, he was a postdoctoral researcher in Distributed Electrical Systems Laboratory, EPFL. Since 2017, he has been with the National Grid ESO, Warwick, UK, where he is currently a team lead on power systems analysis, optimisation, and advanced analytics.

Amin Kaboli

Dr. Amin Kaboli is a lecturer at Swiss Federal Institute of Technology in Lausanne (EPFL), specialising in AI Product Management, Production Management, and Continuous Improvement. He coaches and advises tech founders and business executives across industries, holding advanced leadership diplomas from IMD Business School and a Ph.D. in Manufacturing Systems & Robotics from EPFL.