Grammatical Evolution for Temperature Prediction Models in Different Photovoltaic Technologies

Created by W.Langdon from gp-bibliography.bib Revision:1.8528

@InProceedings{cortes-llanos:2025:GECCOcomp,

• author = "Alexander Cortes-Llanos and Lucia Serrano-Lujan and Carlos Toledo and Antonio Urbina and J. Manuel Colmenar",
• title = "Grammatical Evolution for Temperature Prediction Models in Different Photovoltaic Technologies",
• booktitle = "10th Workshop on Industrial Applications of Metaheuristics (IAM 2025)",
• year = "2025",
• editor = "Silvino Fernandez Alzueta and Pablo Valledor Pellicer and Thomas Stuetzle",
• pages = "2182--2189",
• address = "Malaga, Spain",
• series = "GECCO '25 Companion",
• month = "14-18 " # jul,
• organisation = "SIGEVO",
• publisher = "Association for Computing Machinery",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, grammatical evolution, differential evolution, prediction models",
• isbn13 = "979-8-4007-1464-1",
• URL = "https://doi.org/10.1145/3712255.3734544",
• DOI = "doi:10.1145/3712255.3734544",
• size = "8 pages",
• abstract = "This work presents a comparative analysis of thermal models for photovoltaic modules using Grammatical Evolution (GE) and Differential Evolution (DE) across four photovoltaic (PV) technologies: crystalline silicon (c-Si), amorphous silicon (a-Si), cadmium telluride (CdTe), and organic (OPV), under three sky conditions: sunny, cloudy, and diffuse. Temperature data were collected through a monitoring system in a photovoltaic cube, measuring temperatures on the horizontal face (top PV module) as well as environmental parameters (irradiance, ambient temperature, wind speed and direction, relative humidity). Three empirical models from the literature (Sandia, Faiman, and Obiwulu) were compared with 10 models generated by GE+DE using a Global Performance Index (GPI) to evaluate the accuracy of the models, considering five statistical metrics. The results show that in 11 out of 12 scenarios, the generated models outperform the empirical models, highlighting the importance of relative humidity in model accuracy. This work extends previous research, providing more accurate predictive models for the operating temperature of photovoltaic modules.",
• notes = "GECCO-2025 IAM workshop A Recombination of the 34th International Conference on Genetic Algorithms (ICGA) and the 30th Annual Genetic Programming Conference (GP)",

}

Genetic Programming entries for Alexander Cortes-Llanos Lucia Serrano-Lujan Carlos Toledo Antonio Urbina J Manuel Colmenar

Citations