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Simon Camal

Simon Camal

Research officer

Center · PERSEE

Discipline(s)
Applied Mathematics, Computer Science, Electrical Engineering, Electronics, Electromagnetics, Photonics and Systems, Energy, Thermal Sciences, Signal, Image, Automatic Control, Robotics and Industrial Engineering
Topic(s)
Grid/Network, Hydrogen, Renewable Energy, Scenario-Modelling

Keywords

Awards & distinctions

  • 2022 Visiting Grant for a guest stay at the National Center for Atmospheric Research (NCAR, Boulder, Colorado, USA)
  • 2022 1st international thesis prize "EDP Labelec Merit Award"
  • 2020 1st prize for the 2020 thesis of the Think SmartGrids (TSG) Association,

Team

ERSEI - Energies Renouvelables et Systèmes Energétiques Intelligents

Biography

Simon Camal is a researcher specializing in energy systems and the integration of renewable energy. His work focuses on optimizing decision-making under uncertainty, particularly through innovative approaches such as decision-focused learning and stochastic methods applied to electricity markets. His expertise covers modeling the interactions between renewable generation, storage, and grid flexibility, as well as analyzing the socioeconomic impacts on energy demand. He has contributed to methodological advances, notably in probabilistic forecasting, distributed energy resource management, and data privacy preservation in collaborative systems. His research draws on tools from machine learning, stochastic optimization, and text data analysis to improve the resilience and efficiency of energy infrastructure.

Publication(s)

Projects

  • 2019-2023 Smart4RES - Science des données pour la prévision de la production d'énergie renouvelable Co-Investigator The digitalisation of the energy sector has generated a new landscape featuring the emergence of a wealth of data that were previously not available. The exploitation of these data (in combination with current computational and data storage capabilities), through new adapted modelling tools, is the key to change the game in RES forecasting but also for the decision-making tools that use these forecasts in end-use applications. The knowledge created will be the foundation for the next generation of modelling and short-term forecasting tools of weather-dependent RES power production and related decision making. Project aims: • an increase of at least 15% in RES forecasting performance and, • leverage the economic value of RES forecasting by considering the whole value chain from weather forecasting to end-use applications. To achieve this vision, Smart4RES proposes disruptive research ideas with the objective to reach breakthrough rather than incremental improvements. doi: https://doi.org/10.3030/864337 website: https://www.smart4res.eu/
  • 2023 - 2028 AI-NRGY (PEPR TASE) - Distributed AI architecture for future energy systems integrating a large number of distributed sources Partner AI-NRGY’s objective is to address the major constraints of tomorrow’s energy networks (highly distributed, dynamic, heterogeneous, critical and sometimes volatile) by contributing to the implementation of distributed intelligence solutions taking advantage of different computing methods (edge, fog and cloud computing), and by proposing a software architecture as well as the methods, models and algorithms needed to implement distributed intelligence solutions likely to accelerate the digitization of energy networks. ANR reference : 22-PETA-0004 https://www.pepr-tase.fr/en/projet/ai-nrgy_en/
  • 2023 - 2027 FINE4CAST - New generation of tools for forecasting energy demand and renewable energy production on fine spatial and temporal scales Co-Investigator The main objective of the Fine4Cast project is to improve short-term forecasting (minutes to days) of renewable energy production and consumption on a fine geographic scale (production plants, consumers, territories). Improved forecasts are essential if more renewable energies are to be integrated into power systems. The emergence of new players and new use cases means a need for new products. Forecasters face new challenges due to the proliferation of data sources, and the associated threats and confidentiality constraints. Fine4Cast offers a holistic approach that covers the entire value and modeling chain of energy forecasting, from data to weather and energy forecasts, and including the optimal use of forecasts for decision-making in power systems and the energy market. ANR reference : 22-PETA-0008 https://www.pepr-tase.fr/en/projet/fine4cast_en/
  • 2023 - 2028 FlexTASE - Flexibility for Advanced Energy Systems Technologies Partner Faced with the intermittency of renewable energies (sun, wind, etc.) and the risk of congestion or insufficiency of available means of production, flexibility – which makes it possible to orchestrate a balance between production and consumption – is a challenge that calls for both technical and social innovations to move towards the involvement/appropriation of all players in the energy chain, from managers to consumers. The aim of the FLEXTASE project is to develop a new paradigmatic technical and social approach to address two types of flexibility: - Indirect (or implicit) flexibility, which mobilizes players around technical and organizational solutions by sending signals (tariff or otherwise), - Direct (or explicit) flexibility, involving the control of production, consumption or storage systems, managed directly by algorithms, systems and players (such as aggregators). ANR reference : 22-PETA-0009 https://www.pepr-tase.fr/en/projet/flextase_en/

PhD supervision

  • 2025 Machine learning methods leveraging big data for very short-term forecasting under real-world conditions of renewable production LIGNEREUX Valentin
  • 2025 Development of prescriptive analysis methods based on AI for optimizing energy systems LE STANC Stevan
  • 2025 Quantum machine learning and computational algorithms for optimizing low-carbon energy systems GIANCOLI Corrado
  • 2024 Towards a seamless generic approach to local-scale forecasting of renewable production using multiple data sources MÁRQUEZ ALVAREZ Victor Manuel
  • 2024 Towards a generic AI-based approach for local-scale energy consumption forecasting in the context of smart grids CHIBOUT Erwan
  • 2022 Textual data for advanced modeling of electricity demand and price dynamics BAI Yun
  • 2022 Decision support method for the development and contracting of green hydrogen production systems. PALMER Owen
  • 2022 Data-sharing methods that respect privacy and confidentiality constraints for optimizing multi-actor energy systems STIPPEL Lukas
  • 2021 Optimization of the potential for energy demand flexibility in the industrial sector LEDUR Sylvain
  • 2021 Advanced methods to improve the interpretability of AI tools with applications in the energy sector PARGINOS Konstantinos
  • 2021 Stochastic Distributed Optimization for Managing Complex Virtual Power Plants: Valuing the Flexibility of a Run-of-River Hydropower Chain for Renewable Energy Integration SANTOSUOSSO Luca