Short-term load forecasting for smart water and gas grids: A comparative evaluation
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- @InProceedings{Fagiani:2015:ieeeEEEIC,
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author = "Marco Fagiani and Stefano Squartini and
Roberto Bonfigli and Francesco Piazza",
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booktitle = "15th IEEE International Conference on Environment and
Electrical Engineering (EEEIC)",
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title = "Short-term load forecasting for smart water and gas
grids: A comparative evaluation",
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year = "2015",
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pages = "1198--1203",
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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.",
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/EEEIC.2015.7165339",
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month = jun,
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notes = "Also known as \cite{7165339}",
- }
Genetic Programming entries for
Marco Fagiani
Stefano Squartini
Roberto Bonfigli
Francesco Piazza
Citations