Optimizing wind energy trading decisions using interpretable AI-based tools: The symbolic regression approach
| Autor: | Parginos, Konstantinos, Camal, Simon, Bessa, Ricardo, Kariniotakis, Georges |
|---|---|
| Přispěvatelé: | Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques (PERSEE), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Institute for Systems and Computer Engineering, Technology and Science [Porto] (INESC TEC), WindEurope, European Project: 945304,Ai4theSciences, European Project: 864337,Smart4RES |
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Electricity markets
Artificial intelligence Energy trading XAI [SPI.ENERG]Engineering Sciences [physics]/domain_spi.energ AI Explainable AI Wind farms Renewable Energy [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC] Day-Ahead market Wind energy [MATH.APPL]Mathematics [math]/domain_math.appl Forecasting Interpretable IA |
| Zdroj: | WindEurope Annual Event 2023 WindEurope Annual Event 2023, Apr 2023, Copenhagen, Denmark. 2023 |
| Popis: | International audience; Challenging times for European electricity security have recently brought light to the importance of a robust power sector with well-functioning electricity markets. Increased uncertainty disrupts the standard practice of decision-making in energy systems. The increased penetration of Renewable Energy Sources (RES) such as wind and photovoltaic plants adds to this uncertainty due to the weather dependency of their electricity production. Artificial Intelligence (AI) based tools have proven their efficiency in different applications in the energy sector ranging from forecasting to optimization and decision making. They permit to simplify modeling chains and to improve performance due to higher learning capabilities compared to state-of-the-art methods. However, decision-makers of the energy sector, especially in high-risk situations, need to understand how decision-aid tools construct their outputs from the data. AI-based tools are often seen as black-box models and this penalizes their acceptability by end-users (traders, power system operators a.o.). The lack of interpretability of AI tools is a major challenge for the wider adoption of AI in the energy sector and a fundamental requirement to better support humans in the decision-aid process. Agents of energy systems expect very high levels of reliability for the various services they provide. As energy systems are impacted by multiple uncertainty sources (e.g. available power of RES plants, climate change, market conditions), developed AI tools should not only be performant on average situations but be able to guarantee robust solutions in the case of extreme events. Therefore, our research focuses on optimizing understandable symbolic representations of data-driven decision-aid models for human operators in the energy sector. |
| Databáze: | OpenAIRE |
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