Electricity consumption forecasting models for administration buildings of the UK higher education sector

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@Article{Amber:2015:EB,
  author =       "K. P. Amber and M. W. Aslam and S. K. Hussain",
  title =        "Electricity consumption forecasting models for
                 administration buildings of the {UK} higher education
                 sector",
  journal =      "Energy and Buildings",
  volume =       "90",
  pages =        "127--136",
  year =         "2015",
  keywords =     "genetic algorithms, genetic programming, Electricity
                 forecasting, Administration buildings, Multiple
                 regression",
  ISSN =         "0378-7788",
  DOI =          "doi:10.1016/j.enbuild.2015.01.008",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378778815000110",
  abstract =     "Electricity consumption in the administration
                 buildings of a typical higher education campus in the
                 UK accounts for 26percent of the campus annual
                 electricity consumption. A reliable forecast of
                 electricity consumption helps energy managers in
                 numerous ways such as in preparing future energy
                 budgets and setting up energy consumption targets. In
                 this paper, we developed two models, a multiple
                 regression (MR) model and a genetic programming (GP)
                 model to forecast daily electricity consumption of an
                 administration building located at the Southwark campus
                 of London South Bank University in London. Both models
                 integrate five important independent variables, i.e.
                 ambient temperature, solar radiation, relative
                 humidity, wind speed and weekday index. Daily values of
                 these variables were collected from year 2007 to year
                 2013. The data sets from year 2007 to 2012 are used for
                 training the models while 2013 data set is used for
                 testing the models. The predicted test results for both
                 the models are analysed and compared with actual
                 electricity consumption. At the end, some conclusions
                 are drawn about the performance of both models
                 regarding their forecasting capabilities. The results
                 demonstrate that the GP model performs better with a
                 Total Absolute Error (TAE) of 6percent compared to TAE
                 of 7percent for MR model.",
}

Genetic Programming entries for K P Amber Muhammad Waqar Aslam S K Hussain

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