A review of datasets and load forecasting techniques for smart natural gas and water grids: Analysis and experiments

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@Article{Fagiani:2015:Neurocomputing,
  author =       "M. Fagiani and S. Squartini and L. Gabrielli and 
                 S. Spinsante and F. Piazza",
  title =        "A review of datasets and load forecasting techniques
                 for smart natural gas and water grids: Analysis and
                 experiments",
  journal =      "Neurocomputing",
  volume =       "170",
  pages =        "448--465",
  year =         "2015",
  note =         "Advances on Biological Rhythmic Pattern Generation:
                 Experiments, Algorithms and Applications, Selected
                 Papers from the 2013 International Conference on
                 Intelligence Science and Big Data Engineering (IScIDE
                 2013)Computational Energy Management in Smart Grids",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2015.04.098",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231215009297",
  abstract =     "In this paper, experiments concerning the prediction
                 of water and natural gas consumption are presented,
                 focusing on how to exploit data heterogeneity to get a
                 reliable outcome. Prior to this, an up-to-date
                 state-of-the-art review on the available datasets and
                 forecasting techniques of water and natural gas
                 consumption, is conducted. A collection of techniques
                 (Artificial Neural Networks, Deep Belief Networks, Echo
                 State Networks, Support Vector Regression, Genetic
                 Programming and Extended Kalman Filter-Genetic
                 Programming), partially selected from the
                 state-of-the-art ones, are evaluated using the few
                 publicly available datasets. The tests are performed
                 according to two key aspects: homogeneous evaluation
                 criteria and application of heterogeneous data.
                 Experiments with heterogeneous data obtained combining
                 multiple types of resources (water, gas, energy and
                 temperature), aimed to short-term prediction, have been
                 possible using the Almanac of Minutely Power dataset
                 (AMPds). On the contrary, the Energy Information
                 Administration (E.I.A.) data are used for long-term
                 prediction combining gas and temperature information.
                 At the end, the selected approaches have been evaluated
                 using the sole Tehran water consumption for long-term
                 forecasts (thanks to the full availability of the
                 dataset). The AMPds and E.I.A. natural gas results show
                 a correlation with temperature, that produce a
                 performance improvement. The ANN and SVR approaches
                 achieved good performance for both long/short-term
                 predictions, while the EKF-GP showed good outcomes with
                 the E.I.A. datasets. Finally, it is the authors times'
                 purpose to create a valid starting point for future
                 works that aim to develop innovative forecasting
                 approaches, providing a fair comparison among different
                 computational intelligence and machine learning
                 techniques.",
  keywords =     "genetic algorithms, genetic programming, Heterogeneous
                 data forecasting, Short/long-term load forecasting,
                 Smart water/gas grid, Forecasting techniques,
                 Computational intelligence, Machine learning",
}

Genetic Programming entries for Marco Fagiani Stefano Squartini Leonardo Gabrielli Susanna Spinsante Francesco Piazza

Citations