- PhD Student
I am a first-year PhD student and Gates Scholar in Computer Science (Energy and Environment Group). I am supervised by Srinivasan Keshav. My research investigates the use of Machine Learning for the decarbonisation of buildings.
Previously, I have explored the design and operation of sustainable energy systems for residential homes, incorporating technologies such as solar PV, stationary batteries, and bidirectional EVs. My work led to the development of SOPEVS, an innovative algorithm that personalises the joint sizing and operation of solar PV and storage for single-family homes. Additionally, I created SPAGHETTI, a synthetic data generation tool for modelling EV usage traces.
Currently, my research is centred on data-driven strategies for retrofitting and optimising the operation of commercial buildings.
Check out my website (http://anaïsberkes.com) for a short overview of my research.
Biography
Career
2021-2022: Product Team, Archlet.
2021-2022: Partner, Wingman Campusfund.
2021-2022: Teaching assistant. ETH Zürich Department of Computer Science.
2019: Machine Learning software development intern. Ghelia - Sony Computer Science Laboratories.
Qualifications
2023: Bsc. in Computer Science, ETH Zürich
Awards
Gates Cambridge Scholar
Heidelberg Laureate Forum
Lauréate du Concours Général
Research
Building control and operation, Sizing and operating of residential energy systems, Data-driven retrofit strategies, Reinforcement Learning, Remote Sensing.
Publications
- Anaïs Berkes and Srinivasan Keshav. 2024. SOPEVS: Sizing and Operation of PV-EV-Integrated Modern Homes. In Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems (e-Energy '24). Association for Computing Machinery, New York, NY, USA, 14–26. https://doi.org/10.1145/3632775.3661941
- Berkes, A. and Keshav, S., 2024. SPAGHETTI: a synthetic data generator for post-Covid electric vehicle usage. _Energy Informatics_, _7_(1), pp.1-21.
