Assessment of Exogenous Variables on Intra-Day Solar Irradiance Forecasting Models

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@InProceedings{dePaiva:2018:ieeeEEEIC,

• author = "Gabriel Mendonca {de Paiva} and Sergio Pires Pimentel and Sonia Leva and Marco Mussetta",
• booktitle = "2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I CPS Europe)",
• title = "Assessment of Exogenous Variables on Intra-Day Solar Irradiance Forecasting Models",
• year = "2018",
• abstract = "Accurate and practical forecasting models are very important as tools for optimal integration of the solar energy source in smart grids. This work presents a comparison of four models of intra-day radiance forecasting based on genetic programming. These models are evaluated at two distinct locations, with completely different climate characteristics, with data structured in 10-minute averages to forecast irradiance up to 180 minutes ahead. The models differ in the addition of exogenous weather variables or exogenous deterministic irradiance components. With the use of genetic programming, and at these specific locations, the addition of exogenous weather variables did not result in permanent accuracy improvement, while addition of the deterministic irradiance component did.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/EEEIC.2018.8493938",
• month = jun,
• notes = "Also known as \cite{8493938}",

}

Genetic Programming entries for Gabriel Mendonca de Paiva Sergio Pires Pimentel Sonia Leva Marco Mussetta

Citations