Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks
This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is ap...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | en_US |
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American Society of Civil Engineers
2012
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| Online Access: | http://hdl.handle.net/1721.1/69965 https://orcid.org/0000-0001-5358-4140 |
| _version_ | 1826211047015448576 |
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| author | Preis, Ami Whittle, Andrew Ostfeld, Avi Perelman, Lina |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Preis, Ami Whittle, Andrew Ostfeld, Avi Perelman, Lina |
| author_sort | Preis, Ami |
| collection | MIT |
| description | This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system–aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system. |
| first_indexed | 2024-09-23T14:59:43Z |
| format | Article |
| id | mit-1721.1/69965 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T14:59:43Z |
| publishDate | 2012 |
| publisher | American Society of Civil Engineers |
| record_format | dspace |
| spelling | mit-1721.1/699652022-09-29T11:55:35Z Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks Preis, Ami Whittle, Andrew Ostfeld, Avi Perelman, Lina Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Singapore-MIT Alliance in Research and Technology (SMART) Whittle, Andrew Preis, Ami Whittle, Andrew This paper describes and demonstrates an efficient method for online hydraulic state estimation in urban water networks. The proposed method employs an online predictor-corrector (PC) procedure for forecasting future water demands. A statistical data-driven algorithm (M5 Model-Trees algorithm) is applied to estimate future water demands, and an evolutionary optimization technique (genetic algorithms) is used to correct these predictions with online monitoring data. The calibration problem is solved using a modified least-squares (LS) fit method (Huber function) in which the objective function is the minimization of the residuals between predicted and measured pressure at several system locations, with the decision variables being the hourly variations in water demands. To meet the computational efficiency requirements of real-time hydraulic state estimation for prototype urban networks that typically comprise tens of thousands of links and nodes, a reduced model is introduced using a water system–aggregation technique. The reduced model achieves a high-fidelity representation for the hydraulic performance of the complete network, but greatly simplifies the computation of the PC loop and facilitates the implementation of the online model. The proposed methodology is demonstrated on a prototypical municipal water-distribution system. Singapore-MIT Alliance for Research and Technology (Center for Environmental Sensing and Modeling (CENSAM)) Singapore. National Research Foundation 2012-04-05T19:56:05Z 2012-04-05T19:56:05Z 2011-07 2011-01 Article http://purl.org/eprint/type/JournalArticle 0733-9496 1943-5452 http://hdl.handle.net/1721.1/69965 Preis, Ami et al. “Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks.” Journal of Water Resources Planning and Management 137.4 (2011): 343. https://orcid.org/0000-0001-5358-4140 en_US http://dx.doi.org/10.1061/(ASCE)WR.1943-5452.0000113 Journal of Water Resources Planning and Management Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf American Society of Civil Engineers Whittle via Anne Graham |
| spellingShingle | Preis, Ami Whittle, Andrew Ostfeld, Avi Perelman, Lina Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title | Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title_full | Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title_fullStr | Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title_full_unstemmed | Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title_short | Efficient Hydraulic State Estimation Technique Using Reduced Models of Urban Water Networks |
| title_sort | efficient hydraulic state estimation technique using reduced models of urban water networks |
| url | http://hdl.handle.net/1721.1/69965 https://orcid.org/0000-0001-5358-4140 |
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