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