Interpretable data-driven solar power plant trading strategies
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- @InProceedings{Parginos:2022:ISGT-Europe,
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author = "Konstantinos Parginos and Ricardo Bessa and
Simon Camal and Georges Kariniotakis",
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booktitle = "2022 IEEE PES Innovative Smart Grid Technologies
Conference Europe (ISGT-Europe)",
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title = "Interpretable data-driven solar power plant trading
strategies",
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year = "2022",
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address = "Novi Sad, Serbia",
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month = "10-12 " # oct,
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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,",
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URL = "https://hal.science/hal-03772848",
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DOI = "doi:10.1109/ISGT-Europe54678.2022.9960432",
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size = "5 pages",
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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