Wind power forecasting: An application of machine learning in renewable energy

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Gul Muhammad Khan and Jawad Ali and 
                 Sahibzada Ali Mahmud",
  booktitle =    "International Joint Conference on Neural Networks
                 (IJCNN 2014)",
  title =        "Wind power forecasting: An application of machine
                 learning in renewable energy",
  year =         "2014",
  month =        jul,
  pages =        "1130--1137",
  DOI =          "doi:10.1109/IJCNN.2014.6889771",
  size =         "8 pages",
  abstract =     "The advancement in renewable energy sector being the
                 focus of research these days, a novel neuro
                 evolutionary technique is proposed for modelling wind
                 power forecasters. The paper uses the robust technique
                 of Cartesian Genetic Programming to evolve ANN for
                 development of forecasting models. These Models
                 predicts power generation of a wind based power plant
                 from a single hour up to a year - taking a big lead
                 over other proposed models by reducing its MAPE to as
                 low as 1.049percent for a single day hourly prediction.
                 Results when compared with other models in the
                 literature demonstrated that the proposed models are
                 among the best estimators of wind based power
                 generation plants proposed to date.",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  notes =        "Dept. of Electr. Eng., Univ. of Eng. & Technol.,
                 Peshawar, Pakistan

                 Also known as \cite{6889771}",

Genetic Programming entries for Gul Muhammad Khan Jawad Ali Sahibzada Ali Mahmud