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,
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author = "M. Fagiani and S. Squartini and L. Gabrielli and
S. Spinsante and F. Piazza",
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title = "A review of datasets and load forecasting techniques
for smart natural gas and water grids: Analysis and
experiments",
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journal = "Neurocomputing",
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volume = "170",
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pages = "448--465",
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year = "2015",
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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",
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ISSN = "0925-2312",
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DOI = "doi:10.1016/j.neucom.2015.04.098",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925231215009297",
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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.",
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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