IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Water resources and hydroinformatics : Efficient energy management in water distribution through applying case-basedreasoning (cbr)
Efficient energy management in water distribution through applying case-basedreasoning (cbr)
Currently, Decision Support Tools (DST) for the energy management in water distribution systems have to deal with
complex and variable interrelations between large quantities of variables, making difficult the operational usage of
empirical models to perform competitive results. Most of the explored solutions are based on complex optimization models
designed for controlled situations or simplified water networks, due to difficulties in obtaining needed data and the
adaptability of the model to changes that frequently undergo in the water network. The work presented tackles this
situation by the application of a totally different technological strategy based on a cognitive intelligence that learns from
operational situations to achieve incremental energy savings. Specifically, the learning is performed by finding past
situations (cases) based on the similarity between past and forecasted daily demands, accomplishing minimum energy
consumption. This inference engine, known as CBR, is aimed at recommending suitable pumping scheduling by using a
combination of machine learning techniques (clustering, classification and windowing) to accomplish suitable case
retrieval according to current water distribution situation. Case retrieval considers lesser energy consumption produced by
the expected demand curves to return the case. After this, water manager performs modifications according to his
experience and simulations in hydraulic tools to revise the returned case. Then, revised cases are evaluated by the CBR
and learning is performed by those cases that improve the stored casesĄ¯ solution without compromising the water
distribution network. The developed CBR takes part of a DST aimed at using decisional processes knowledge, existing in
the water supply distribution chain, to improve daily operations by avoiding water resource mismanagement and inefficient
energy strategies. DST takes advantage of the ICT-WatERP architecture to ensure interoperability, information sharing,
contributing to address water management issues by being part of the multiple-inference engines core. As a result, the
DST shows potentially energy savings around 30% over 2011 demands, against the applied strategies in the Karlsruhe
water distribution network by adjusting the energy consumption to the demand curve.
File Size : 1,748,204 bytes
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Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Water resources and hydroinformatics
Date Published : 20/08/2015
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