Interpretable data-driven solar power plant trading strategies

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Parginos:2022:ISGT-Europe,

• author = "Konstantinos Parginos and Ricardo Bessa and Simon Camal and Georges Kariniotakis",
• booktitle = "2022 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe)",
• title = "Interpretable data-driven solar power plant trading strategies",
• year = "2022",
• address = "Novi Sad, Serbia",
• month = "10-12 " # oct,
• keywords = "genetic algorithms, genetic programming, Photovoltaic systems, Measurement, Analytical models, Costs, Decision making, Wind farms, Artificial Intelligence, AI, XAI, Renewables, Interpretability, Trading, Solar, Symbolic Regression,",
• URL = "https://hal.science/hal-03772848",
• DOI = "doi:10.1109/ISGT-Europe54678.2022.9960432",
• size = "5 pages",
• abstract = "Standard practices of decision-making in energy systems are dynamic, non-linear, complex, and chaotic processes in nature. Trading the power produced by solar photovoltaic (PV) plants in electricity markets is an important decision-making problem which receives increasing attention in the past few decades. The main objective of this paper is to build an interpretable data-driven decision aid model for the case study of a solar power plant with the objective to minimize imbalance costs and thus maximise the revenue, using Symbolic Regression (SR) through Genetic Programming. The use of SR in the experiments and analysis developed in this paper show numerous advantages. SR evolves linear combinations of non-linear functions of the input variables. Three penalty metrics are introduced to enhance the interpretability of the final solutions. SR shows robust results, especially in the case study.",
• notes = "Also known as \cite{9960432}",

}

Genetic Programming entries for Konstantinos Parginos Ricardo Jorge Bessa Simon Camal Georges Kariniotakis

Citations