GP-Prepocessed Fuzzy Inference for The Energy Load Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@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