A hybrid genetic radial basis function network with fuzzy corrector for short term load forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "W. T. Ghareeb and E. F. {El Saadany}",
  booktitle =    "IEEE Electrical Power Energy Conference (EPEC 2013)",
  title =        "A hybrid genetic radial basis function network with
                 fuzzy corrector for short term load forecasting",
  year =         "2013",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/EPEC.2013.6802948",
  size =         "5 pages",
  abstract =     "The short term load forecasting plays a critical role
                 in power system operation and economics. The accuracy
                 of short term load forecasting is very important since
                 it affects generation scheduling and electricity
                 prices, and hence an accurate short term load
                 forecasting method should be used. This paper proposes
                 a Genetic Algorithm optimised Radial Basis Function
                 network (GA-RBF) with a fuzzy corrector for the problem
                 of short term load forecasting. In order to demonstrate
                 this system capability, the system has been compared
                 with four well known techniques in the area of load
                 forecasting. These techniques are the multi-layer feed
                 forward neural network, the RBF network, the adaptive
                 neuro-fuzzy inference System and the genetic
                 programming. The data used in this study is a real data
                 of the Egyptian electrical network. The weather factors
                 represented in the minimum and the maximum daily
                 temperature have been included in this study. The
                 GA-RBF with the fuzzy corrector has successfully
                 forecast the future load with high accuracy compared to
                 that of the other load forecasting techniques included
                 in this study.",
  notes =        "Dept. of Electr. & Comput. Eng., Univ. of Waterloo,
                 Waterloo, ON, Canada

                 Also known as \cite{6802948}",

Genetic Programming entries for Wael Taha Ghareeb Ehab El-Saadany