Prediction of Urban Short-Term Water Consumption in Zhengzhou City
For supplying optimal scheduling of Zhengzhou city with short-term water consumption data, this paper builds three types of forecasting model according to moving arithmetic mean method, regression analysis method and BP neural network. As a result, forecasting result is obtained by water supply data...
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| creator | Jianhua Liu Rui Zhang Lailing Wang |
| description | For supplying optimal scheduling of Zhengzhou city with short-term water consumption data, this paper builds three types of forecasting model according to moving arithmetic mean method, regression analysis method and BP neural network. As a result, forecasting result is obtained by water supply data and meteorological data. The study shows that three different methods all can meet the need of urban water supply project in the prediction of hourly water consumption. Regression analysis and BP neural network can obtain better forecasting result and can gracefully satisfy the request of urban water supply scheduling. If water consumption measured in 15-minute unit, the forecasting result of BP neural network is better, this can meet the urban water supply request better. |
| doi_str_mv | 10.1109/ICICTA.2010.535 |
| format | Conference Proceeding |
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As a result, forecasting result is obtained by water supply data and meteorological data. The study shows that three different methods all can meet the need of urban water supply project in the prediction of hourly water consumption. Regression analysis and BP neural network can obtain better forecasting result and can gracefully satisfy the request of urban water supply scheduling. If water consumption measured in 15-minute unit, the forecasting result of BP neural network is better, this can meet the urban water supply request better.</description><identifier>ISBN: 9781424472796</identifier><identifier>ISBN: 1424472792</identifier><identifier>EISBN: 9781424472802</identifier><identifier>EISBN: 1424472806</identifier><identifier>DOI: 10.1109/ICICTA.2010.535</identifier><identifier>LCCN: 2010928758</identifier><language>eng</language><publisher>IEEE</publisher><subject>Cities and towns ; Economic forecasting ; Meteorology ; neural network ; Neural networks ; Optimal scheduling ; Predictive models ; Regression analysis ; Time series analysis ; urban water supply system ; Water conservation ; water consumption forecast ; Weather forecasting</subject><ispartof>2010 International Conference on Intelligent Computation Technology and Automation, 2010, Vol.2, p.922-926</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5522593$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5522593$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Jianhua Liu</creatorcontrib><creatorcontrib>Rui Zhang</creatorcontrib><creatorcontrib>Lailing Wang</creatorcontrib><title>Prediction of Urban Short-Term Water Consumption in Zhengzhou City</title><title>2010 International Conference on Intelligent Computation Technology and Automation</title><addtitle>ICICTA</addtitle><description>For supplying optimal scheduling of Zhengzhou city with short-term water consumption data, this paper builds three types of forecasting model according to moving arithmetic mean method, regression analysis method and BP neural network. As a result, forecasting result is obtained by water supply data and meteorological data. The study shows that three different methods all can meet the need of urban water supply project in the prediction of hourly water consumption. Regression analysis and BP neural network can obtain better forecasting result and can gracefully satisfy the request of urban water supply scheduling. If water consumption measured in 15-minute unit, the forecasting result of BP neural network is better, this can meet the urban water supply request better.</description><subject>Cities and towns</subject><subject>Economic forecasting</subject><subject>Meteorology</subject><subject>neural network</subject><subject>Neural networks</subject><subject>Optimal scheduling</subject><subject>Predictive models</subject><subject>Regression analysis</subject><subject>Time series analysis</subject><subject>urban water supply system</subject><subject>Water conservation</subject><subject>water consumption forecast</subject><subject>Weather forecasting</subject><isbn>9781424472796</isbn><isbn>1424472792</isbn><isbn>9781424472802</isbn><isbn>1424472806</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2010</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNj0tLxDAcxCOyoK49e_CSL9D1nzRpkuMafBQWFKwIXpY0Dxux7ZJ2D-untz4OzmWYH8PAIHRBYEUIqKtKV7peryjMgBf8CGVKSMIoY4JKoMf_s1DlAp19VxWVgssTlI3jO8xinM74FF0_Ju-ineLQ4yHg59SYHj-1Q5ry2qcOv5jJJ6yHftx3u59W7PFr6_u3z3bYYx2nwzlaBPMx-uzPl6i-van1fb55uKv0epNHBVMuwBjmGiitI-CVASt5Q60KwgUIpRPSEWWJJ44FXjQy8MCla5i1AiwoWyzR5e9s9N5vdyl2Jh22fP7BVVF8AeynTh4</recordid><startdate>201005</startdate><enddate>201005</enddate><creator>Jianhua Liu</creator><creator>Rui Zhang</creator><creator>Lailing Wang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201005</creationdate><title>Prediction of Urban Short-Term Water Consumption in Zhengzhou City</title><author>Jianhua Liu ; Rui Zhang ; Lailing Wang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i90t-70aa4db06cd10e9a0c85b2c9f7df0f6d78d19c1e1d4f53b8f5f58db4cc70c09c3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Cities and towns</topic><topic>Economic forecasting</topic><topic>Meteorology</topic><topic>neural network</topic><topic>Neural networks</topic><topic>Optimal scheduling</topic><topic>Predictive models</topic><topic>Regression analysis</topic><topic>Time series analysis</topic><topic>urban water supply system</topic><topic>Water conservation</topic><topic>water consumption forecast</topic><topic>Weather forecasting</topic><toplevel>online_resources</toplevel><creatorcontrib>Jianhua Liu</creatorcontrib><creatorcontrib>Rui Zhang</creatorcontrib><creatorcontrib>Lailing Wang</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Jianhua Liu</au><au>Rui Zhang</au><au>Lailing Wang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Prediction of Urban Short-Term Water Consumption in Zhengzhou City</atitle><btitle>2010 International Conference on Intelligent Computation Technology and Automation</btitle><stitle>ICICTA</stitle><date>2010-05</date><risdate>2010</risdate><volume>2</volume><spage>922</spage><epage>926</epage><pages>922-926</pages><isbn>9781424472796</isbn><isbn>1424472792</isbn><eisbn>9781424472802</eisbn><eisbn>1424472806</eisbn><abstract>For supplying optimal scheduling of Zhengzhou city with short-term water consumption data, this paper builds three types of forecasting model according to moving arithmetic mean method, regression analysis method and BP neural network. As a result, forecasting result is obtained by water supply data and meteorological data. The study shows that three different methods all can meet the need of urban water supply project in the prediction of hourly water consumption. Regression analysis and BP neural network can obtain better forecasting result and can gracefully satisfy the request of urban water supply scheduling. If water consumption measured in 15-minute unit, the forecasting result of BP neural network is better, this can meet the urban water supply request better.</abstract><pub>IEEE</pub><doi>10.1109/ICICTA.2010.535</doi><tpages>5</tpages></addata></record> |
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| subjects | Cities and towns Economic forecasting Meteorology neural network Neural networks Optimal scheduling Predictive models Regression analysis Time series analysis urban water supply system Water conservation water consumption forecast Weather forecasting |
| title | Prediction of Urban Short-Term Water Consumption in Zhengzhou City |
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