Short-term load forecasting for smart water and gas grids: A comparative evaluation

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@InProceedings{Fagiani:2015:ieeeEEEIC,
  author =       "Marco Fagiani and Stefano Squartini and 
                 Roberto Bonfigli and Francesco Piazza",
  booktitle =    "15th IEEE International Conference on Environment and
                 Electrical Engineering (EEEIC)",
  title =        "Short-term load forecasting for smart water and gas
                 grids: A comparative evaluation",
  year =         "2015",
  pages =        "1198--1203",
  abstract =     "Moving from a recent publication of Fagiani et al.
                 [1], short-term predictions of water and natural gas
                 consumption are performed exploiting state-of-the-art
                 techniques. Specifically, for two datasets, the
                 performance of Support Vector Regression (SVR), Extreme
                 Learning Machine (ELM), Genetic Programming (GP),
                 Artificial Neural Networks (ANNs), Echo State Networks
                 (ESNs), and Deep Belief Networks (DBNs) are compared
                 adopting common evaluation criteria. Concerning the
                 datasets, the Almanac of Minutely Power Dataset (AMPds)
                 is used to compute predictions with domestic
                 consumption, 2 year of recordings, and to perform
                 further evaluations with the available heterogeneous
                 data, such as energy and temperature. Whereas,
                 predictions of building consumption are performed with
                 the datasets recorded at the Department for
                 International Development (DFID). In addition, the
                 results achieved for the previous release of the AMPds,
                 1 year of recordings, are also reported, in order to
                 evaluate the impact of seasonality in forecasting
                 performance. Finally, the achieved results validate the
                 suitability of ANN, SVR and ELM approaches for
                 prediction applications in small-grid scenario.
                 Specifically, for the domestic consumption the best
                 performance are achieved by SVR and ANN, for natural
                 gas and water, respectively. Whereas, the ANN shows the
                 best results for both water and natural gas forecasting
                 in building scenario.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/EEEIC.2015.7165339",
  month =        jun,
  notes =        "Also known as \cite{7165339}",
}

Genetic Programming entries for Marco Fagiani Stefano Squartini Roberto Bonfigli Francesco Piazza

Citations