GP-Prepocessed Fuzzy Inference for The Energy Load Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{kubota:2000:gfitelp,
  author =       "Naoyuki Kubota and Setsuo Hashimoto and 
                 Fumio Kojima and Kazuhiko Taniguchi",
  title =        "GP-Prepocessed Fuzzy Inference for The Energy Load
                 Prediction",
  booktitle =    "Proceedings of the 2000 Congress on Evolutionary
                 Computation CEC00",
  year =         "2000",
  pages =        "1--6",
  volume =       "1",
  address =      "La Jolla Marriott Hotel La Jolla, California, USA",
  publisher_address = "445 Hoes Lane, P.O. Box 1331, Piscataway, NJ
                 08855-1331, USA",
  month =        "6-9 " # jul,
  organisation = "IEEE Neural Network Council (NNC), Evolutionary
                 Programming Society (EPS), Institution of Electrical
                 Engineers (IEE)",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, hybrid
                 systems, building energy load prediction, energy load
                 prediction, feature extraction, genetic
                 programming-preprocessed fuzzy inference, multivariate
                 statistical analysis, prediction system, building
                 management systems, feature extraction, fuzzy logic,
                 inference mechanisms, load forecasting",
  ISBN =         "0-7803-6375-2",
  DOI =          "doi:10.1109/CEC.2000.870268",
  abstract =     "This paper deals with a prediction system based on
                 genetic programming and fuzzy inference system. In real
                 problems with many parameters, the prediction
                 performance depends on the feature extraction and
                 selection. These processes are performed using methods
                 of multivariate statistical analysis by human
                 operators. However, we should automatically perform
                 feature extraction and selection from many measured
                 data. This paper applies genetic programming for the
                 feature extraction and selection, and further use fuzzy
                 inference for the building energy load prediction. The
                 functions generated by GP translate the measured data
                 into the meaningful information that is used as input
                 data to the fuzzy inference system. The simulation
                 results show that the proposed method can extract
                 meaningful information from the measured data and can
                 predict the building energy load of the next day.",
  notes =        "CEC-2000 - A joint meeting of the IEEE, Evolutionary
                 Programming Society, Galesia, and the IEE.

                 IEEE Catalog Number = 00TH8512,

                 Library of Congress Number = 00-018644",
}

Genetic Programming entries for Naoyuki Kubota Setsuo Hashimoto Fumio Kojima Kazuhiko Taniguchi

Citations