Pressure-based forecasting of next-day solar energy availability using evolutionary fuzzy rules

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Musilek:2015:FUZZ-IEEE,
  author =       "Petr Musilek and Pavel Kroemer and James Rodway and 
                 Michal Prauzek",
  booktitle =    "2015 IEEE International Conference on Fuzzy Systems
                 (FUZZ-IEEE)",
  title =        "Pressure-based forecasting of next-day solar energy
                 availability using evolutionary fuzzy rules",
  year =         "2015",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/FUZZ-IEEE.2015.7337938",
  abstract =     "Prediction of solar energy availability is an
                 important part of a number of applications, from
                 weather and climate modelling, to forecasting power
                 output of photovoltaic cells. This work proposes and
                 evaluates the use of evolutionary fuzzy rules,
                 discovered by genetic programming, for accurate and
                 inexpensive forecasting of next day available solar
                 energy, based solely on atmospheric pressure. The use
                 of only one meteorological variable simplifies the
                 collection, storage and prediction tasks, making this
                 approach suitable for a number of hardware-limited
                 applications. The proposed forecasting system uses a
                 time series of pressure values for the hours leading up
                 to the sunrise of the day for which predictions are
                 made. The pressure values and their differences are
                 submitted to the evolutionary optimiser that combines
                 these values into simple fuzzy rules. The resulting
                 rule set provides predictions that are a substantial
                 improvement over simple analytical estimates. The
                 results of pressure-based forecasters also compare
                 favourably with other simple estimation systems
                 presented in the literature.",
  notes =        "Dept. of Electr. & Comput. Eng., Univ. of Alberta,
                 Edmonton, AB, Canada

                 Also known as \cite{7337938}",
}

Genetic Programming entries for Petr Musilek Pavel Kroemer James Rodway Michal Prauzek

Citations