Pierre Bouquet - Website

I'm a first year PhD Student at MIT CTL where I work under the supervision of Prof. Yossi Sheffi. In the past, I worked with Dr. Amin Kaboli at EPFL.

On-going projects

Get a PhD from MIT
OBAW - one book a week
Project 42

Publications

[OPEN ACCESS] Bouquet, P., Jackson, I., Nick, M., & Amin Kaboli. (2023). AI-based forecasting for optimised solar energy management and smart grid efficiency. International Journal of Production Research, 1–22.
[abstract] [paper] [data]

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.

Conferences

[03.05.2024] SCM.C51. Guest Lecturer. Class: Machine Learning Applications for Supply Chain Management at MIT. AI and the future of jobs – Online job automation risk modelling, forecasting and clustering.

[30.05.2023] MIT CTL Research Conference. AI and the future of jobs – Online job automation risk modelling, forecasting and clustering.

[23.05.2023] 33rd Annual POMS. AI and the future of jobs – Online job automation risk modelling, forecasting and clustering.
Track: [Supply Chain Risk Management], Session: [Uncertainty and Resilience], Abstract code: [115-1552].
[abstract] [POMS 2023]

This presentation introduces an online deep learning and data mining-based framework to assess automation risk across tasks, jobs, and sectors. A five-year forecast and clustering model helps anticipate job evolution, offering valuable insights for stakeholders to guide education, up-skilling, re-skilling, and hiring strategies, as well as identifying high-risk sectors.

News articles

[07.09.21] “The lockdown made it easier to juggle my classwork with my startup”. EPFL NEWS. [article]

[06.05.21] “BioT wins the 10th edition of the START Lausanne Contest”. EPFL NEWS. [article]

[06.05.21] “BioT remporte le 10ème START Lausanne Contest”. startupticker.ch. [article]

Miscellaneous

reading list and notes