GP-based Electricity Price Forecasting

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Alberto Bartoli and Giorgio Davanzo and 
                 Andrea {De Lorenzo} and Eric Medvet",
  title =        "GP-based Electricity Price Forecasting",
  booktitle =    "Proceedings of the 14th European Conference on Genetic
                 Programming, EuroGP 2011",
  year =         "2011",
  month =        "27-29 " # apr,
  editor =       "Sara Silva and James A. Foster and Miguel Nicolau and 
                 Mario Giacobini and Penousal Machado",
  series =       "LNCS",
  volume =       "6621",
  publisher =    "Springer Verlag",
  address =      "Turin, Italy",
  pages =        "37--48",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-20406-7",
  DOI =          "doi:10.1007/978-3-642-20407-4_4",
  abstract =     "The electric power market is increasingly relying on
                 competitive mechanisms taking the form of day-ahead
                 auctions, in which buyers and sellers submit their bids
                 in terms of prices and quantities for each hour of the
                 next day. Methods for electricity price forecasting
                 suitable for these contexts are crucial to the success
                 of any bidding strategy. Such methods have thus become
                 very important in practice, due to the economic
                 relevance of electric power auctions.

                 In this work we propose a novel forecasting method
                 based on Genetic Programming. Key feature of our
                 proposal is the handling of outliers, i.e., regions of
                 the input space rarely seen during the learning. Since
                 a predictor generated with Genetic Programming can
                 hardly provide acceptable performance in these regions,
                 we use a classifier that attempts to determine whether
                 the system is shifting toward a difficult-to-learn
                 region. In those cases, we replace the prediction made
                 by Genetic Programming by a constant value determined
                 during learning and tailored to the specific subregion

                 We evaluate the performance of our proposal against a
                 challenging baseline representative of the
                 state-of-the-art. The baseline analyses a real-world
                 dataset by means of a number of different methods, each
                 calibrated separately for each hour of the day and
                 recalibrated every day on a progressively growing
                 learning set. Our proposal exhibits smaller prediction
                 error, even though we construct one single model, valid
                 for each hour of the day and used unmodified across the
                 entire testing set. We believe that our results are
                 highly promising and may open a broad range of novel
  notes =        "Part of \cite{Silva:2011:GP} EuroGP'2011 held in
                 conjunction with EvoCOP2011 EvoBIO2011 and

Genetic Programming entries for Alberto Bartoli Giorgio Davanzo Andrea De Lorenzo Eric Medvet