Estimación de patrones de consumo de agua potable sectorizadas para la ciudad de Tunja.

dc.contributor.advisorCortés Zambrano, Melquisedec
dc.contributor.authorCante Espitia, Diego Fernando
dc.contributor.corporatenameUniversidad Santo Tomás Tunjaspa
dc.date.accessioned2022-12-13T20:01:51Z
dc.date.available2022-12-13T20:01:51Z
dc.date.issued2022-12-04
dc.descriptionSe realizó la estimación de curvas de consumo de agua potable por sectores para la ciudad de Tunja. Tomando como base las mediciones de caudal registradas por 31 macromedidores correspondientes a 24 sectores hidráulicos que abarcan las 4 zonas de la ciudad (Norte, Centro, Sur y Oriente). Para dicho calculo se usaron en total un aproximado de 450mil mediciones de caudal, tomadas en un periodo de 6 meses comprendido entre el 1 de enero de 2022 y el 30 de junio de 2022. Con estos datos, se realizó un análisis exploratorio y un proceso estadístico para identificar y eliminar los datos atípicos. Se obtuvieron los patrones de consumo para cada uno de los 7 días de la semana en los 24 sectores, adicionalmente, se identificaron las horas pico de consumo de agua potable 6-9am y 6-8pm. Finalmente, se realizó un análisis confirmatorio para determinar una única curva de consumo por sector (24 curvas en total) con las cuales se estimó el consumo diario (en metros cúbicos) y la dotación neta de cada sector bajo las premisas de 3 habitantes por usuario y asumir todos los usuarios como residenciales.spa
dc.description.abstractDrinking water consumption curves were estimated by sectors for the city of Tunja. based on the flow measurements recorded by 31 macrometers corresponding to 24 hydraulic sectors that cover the 4 areas of the city (North, Center, South and East). For this calculation, a total of approximately 450,000 flow measurements were used, taken in a 6-month period between January 1, 2022 and June 30, 2022. With these data, an exploratory analysis and a statistical process were carried out to identify and eliminate outliers. Consumption patterns were obtained for each of the 7 days of the week in the 24 sectors, additionally, the peak hours of drinking water consumption 6-9am and 6-8pm were identified. Finally, a confirmatory analysis was carried out to determine a single consumption curve per sector (24 curves in total) with which the daily consumption (in cubic meters) and the net endowment of each sector were estimated under the premises of 3 inhabitants per user. and assume all users as residential.spa
dc.description.degreelevelPregradospa
dc.description.degreenameIngeniero Civilspa
dc.format.mimetypeapplication/pdf
dc.identifier.citationCante, D. (2022).Estimación de patrones de consumo de agua potable sectorizadas para la ciudad de Tunja. [Tesis de pregrado]. Universidad Santo Tomás, Seccional Tunja.spa
dc.identifier.instnameinstname:Universidad Santo Tomásspa
dc.identifier.reponamereponame:Repositorio Institucional Universidad Santo Tomásspa
dc.identifier.repourlrepourl:https://repository.usta.edu.cospa
dc.identifier.urihttp://hdl.handle.net/11634/48336
dc.language.isospa
dc.publisherUniversidad Santo Tomásspa
dc.publisher.branchCRAI-USTA Tunjaspa
dc.publisher.facultyFacultad de Ingeniería Civilspa
dc.publisher.programPregrado Ingeniería Civilspa
dc.relation.referencesM. S. Rahim, K. A. Nguyen, R. A. Stewart, T. Ahmed, D. Giurco, and M. Blumenstein, “A clustering solution for analyzing residential water consumption patterns,” Knowledge-Based Systems, vol. 233, p. 107522, 2021.spa
dc.relation.referencesG. T. LaVanchy, M. W. Kerwin, and J. K. Adamson, “Beyond ‘Day zero’: Insights and lessons from Cape Town (South Africa),” Hydrogeology Journal, vol. 27, no. 5, pp. 1537–1540, 2019.spa
dc.relation.referencesA. Cominola, K. Nguyen, M. Giuliani, R. A. Stewart, H. R. Maier, and A. Castelletti, “Data mining to uncover heterogeneous water use behaviors from smart meter data,” Water Resources Research, vol. 55, no. 11, pp. 9315–9333, 2019.spa
dc.relation.referencesK. A. Nguyen, R. A. Stewart, H. Zhang, O. Sahin, and N. Siriwardene, “Re- engineering traditional urban water management practices with smart metering and Informatics,” Environmental Modelling & Software, vol. 101, pp. 256–267, 2018.spa
dc.relation.referencesT. R. Gurung, R. A. Stewart, A. K. Sharma, and C. D. Beal, “Smart meters for enhanced water supply network modelling and Infrastructure Planning,” Resources, Conservation and Recycling, vol. 90, pp. 34–50, 2014.spa
dc.relation.referencesT. Boyle, D. Giurco, P. Mukheibir, A. Liu, C. Moy, S. White, and R. Stewart, “Intelligent metering for urban water: A Review,” Water, vol. 5, no. 3, pp. 1052– 1081, 2013.spa
dc.relation.referencesM. S. Rahim, K. A. Nguyen, R. A. Stewart, D. Giurco, and M. Blumenstein, “Advanced household profiling using digital water meters,” Journal of Environmental Management, vol. 288, p. 112377, 2021.spa
dc.relation.referencesS. Patabendige, R. Cardell-Oliver, R. Wang, and W. Liu, “Detection and interpretation of anomalous water use for non-residential customers,” Environmental Modelling & Software, vol. 100, pp. 291–301, 2018.spa
dc.relation.referencesD. Edelmann, T. F. Móri, and G. J. Székely, “On relationships between the Pearson and the distance correlation coefficients,” Statistics & Probability Letters, vol. 169, p. 108960, 2021.spa
dc.relation.referencesM. S. Rahim, K. Anh Nguyen, R. A. Stewart, D. Giurco, and M. Blumenstein, “Predicting household water consumption events: Towards a personalised 195 recommender system to encourage water-conscious behaviour,” 2019 International Joint Conference on Neural Networks (IJCNN), 2019.spa
dc.relation.referencesD. Nettleton, Commercial Data Mining: Processing, analysis and modeling for Predictive Analytics Projects. Waltham, MA: Morgan Kaufmann, 2014.spa
dc.relation.referencesA. P. Almeida, V. Sousa, and C. M. Silva, “Methodology for estimating energy and water consumption patterns in university buildings: Case study, Federal University of Roraima (UFRR),” SSRN Electronic Journal, 2021.spa
dc.relation.referencesMutono, N. et al. (2022) “Spatio-temporal patterns of domestic water distribution, consumption and sufficiency: Neighbourhood Inequalities in Nairobi, Kenya,” Habitat International, 119, p. 102476. Available at: https://doi.org/10.1016/j.habitatint.2021.102476.spa
dc.relation.referencesM. Pacheco, "Aplicación de plataformas tecnológicas y normativas para la gestión técnica del desarrollo urbano en la empresa Veolia aguas de Tunja s.a.e.s.p" Repositorio USTA. https://repository.usta.edu.co/bitstream/handle/11634/33684/2021marilynpachec o.pdf?sequence=4&isAllowed=yspa
dc.relation.referencesMutono, N. et al. (2022) “Spatio-temporal patterns of domestic water distribution, consumption and sufficiency: Neighbourhood Inequalities in Nairobi, Kenya,” Habitat International, 119, p. 102476. Available at: https://doi.org/10.1016/j.habitatint.2021.102476.spa
dc.relation.referencesW. Schultz, S. Javey, and A. Sorokina, “Social comparison as a tool to promote residential water conservation,” Frontiers in Water, vol. 1, 2019.spa
dc.relation.referencesA. Bello-Dambatta, R. Bellini, and P. Williams, “Energy efficiency through household water use efficiency: A survey on public perception of household water and water-related energy use in Ireland,” 2022.spa
dc.relation.referencesTámara Leandro González, Estadística descriptiva Y Probabilidad. Editorial Jorge Tadeo Lozano, 2013.spa
dc.relation.referencesMinVivienda, "Resolución 0330 de 2017", Bogotá: MinVivienda, 2017.spa
dc.relation.referencesBreckenridge, “Loggers • i2o water,” • i2O Water, 24-Apr-2020. [Online]. Disponible: https://es.i2owater.comspa
dc.relation.referencesUNAM, “Capítulo 3.- Sectorización del sistema de agua potable.,” Sectorización del sistema de agua potable. [Online]. Disponible: http://www.ptolomeo.unam.mx:8080/jspui/bitstream/132.248.52.100/566/6/A6.pdf.spa
dc.relation.referencesColombia Agil, “Reporte de Información de Empresas de Energía Al Sistema único de Información de la Superservicios,” Reporte de información de empresas de energía al Sistema Único de Información de la Superservicios | Colombia Ágil. [Online]. Available: https://www.colombiaagil.gov.co/tramites/intervenciones/reporte-de-informacion- de-empresas-de-energia-al-s.spa
dc.relation.referencesLEUCO S.P.A., “Hawk, high pressure piston pumps,” HAWK, high pressure piston pumps. [Online]. Disponible: https://www.hawkpumps.com/en/.spa
dc.relation.referencesS. Ye, J. Wang, H. Fan, and Z. Zhang, “Probabilistic model for truth discovery with mean and median check framework,” Knowledge-Based Systems, vol. 233, p. 107482, 2021.spa
dc.relation.referencesS. Sankaranarayanan, N. Sivakumaran, T. K. Radhakrishnan, and G. Swaminathan, “Dynamic soft sensor based parameters and demand curve estimation for water distribution system: Theoretical and Experimental Cross Validation,” Control Engineering Practice, vol. 102, p. 104544, 2020.spa
dc.relation.referencesB. Du, S. Huang, J. Guo, H. Tang, L. Wang, and S. Zhou, “Interval forecasting for urban water demand using PSO optimized KDE Distribution and LSTM Neural Networks,” Applied Soft Computing, vol. 122, p. 108875, 2022.spa
dc.relation.referencesJ. Yan and T. Tao, “Unsupervised anomaly detection in hourly water demand data using an asymmetric encoder–decoder model,” Journal of Hydrology, vol. 613, p. 128389, 2022.spa
dc.relation.referencesM. Suárez-Varela, “Modeling residential water demand: An approach based on household demand systems,” Journal of Environmental Management, vol. 261, p. 109921, 2020.spa
dc.relation.referencesMa, X.Z., Wu, D., Zhang, S.Q., 2018. Multiple goals dilemma of residential water pricing policy reform: increasing block tariffs or a uniform tariff with rebate? Sustainability 10 (10), 3526.spa
dc.relation.referencesTortajada, C., Gonzalez-Gomez, F., Biswas, A.K., Buurman, J., 2019. Water demand management strategies for water-scarce cities: the case of Spain. Sustain. Cities Soc. 45, 649–656.spa
dc.relation.referencesEscriva-Bou, A., Lund, J.R., Pulido-Velazquez, M., 2015. Optimal residential water conservation strategies considering related energy in California. Water Resour. Res. 51, 4482–4498.spa
dc.rightsAtribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.localAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/co/
dc.subject.keywordDrinking Waterspa
dc.subject.keywordMacrometeringspa
dc.subject.keywordConsumption Patternspa
dc.subject.keywordNet Provisionspa
dc.subject.proposalAgua Potablespa
dc.subject.proposalMacromediciónspa
dc.subject.proposalPatrón de Consumospa
dc.subject.proposalDotación Netaspa
dc.titleEstimación de patrones de consumo de agua potable sectorizadas para la ciudad de Tunja.spa
dc.typebachelor thesis
dc.type.coarhttp://purl.org/coar/resource_type/c_7a1f
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.driveinfo:eu-repo/semantics/bachelorThesis
dc.type.localTesis de pregradospa
dc.type.versioninfo:eu-repo/semantics/acceptedVersion

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