Water consumption forecasting using time series modeling
A case study of a university hospital
DOI:
https://doi.org/10.46421/sispred.v4.8137Keywords:
Water consumption, modeling, forecastingAbstract
Water use in hospitals occurs in various activities, such as human consumption, as well as in cleaning practices, medical therapies, and maintenance. Due to the current need to reduce water consumption in buildings, including healthcare facilities, identifying patterns in water demand and predictive modeling are vital for managers and researchers. In this context, this study aims to apply time series modeling, with the ARIMA, ETS, and Prophet methods, to estimate short-term forecasts of monthly water consumption in a university hospital. Data with monthly frequency from January 2020 to December 2023 were used for modeling, and forecasts for the months of the first half of 2024 were estimated. The results showed that the series is non-stationary, presenting a damped trend, and does not have monthly seasonality. Forecasts with the Prophet model are more accurate, with an average percentage error of 0.14%, when compared to the ETS and ARIMA models, with average percentage errors of 7.54% and 8.51%, respectively.
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ARFANUZZAMAN, M.; RAHMAN, A. A. Sustainable water demand management in the face of rapid urbanization and ground water depletion for social ecological resilience building. Global Ecology and Conservation, v. 10, p. 9–22, 2017. DOI: https://doi.org/10.1016/j.gecco.2017.01.005.
BATISTA, K. J. M.; SILVA, S. R.; RABBANI, E. R. K.; ZLATAR, T. Systematic review of indicators for the assessment of water consumption rates at hospitals. Water Supply, v. 20, 2020. DOI: https://doi.org/10.2166/ws.2019.169.
BOUBAKER, S. Identification of monthly municipal water demand system based on autoregressive integrated moving average model tuned by particle swarm optimization. Journal of Hydroinformatics, v. 19, p. 261–281, 2017. DOI: https://doi.org/10.2166/hydro.2017.035.
BRASIL. Governo Federal. HU-UFSC registra avanços em 2024. Florianópolis SC: Ebserh, 2024. Disponível em: https://www.gov.br/ebserh/pt-br/hospitais-universitarios/regiao-sul/hu-ufsc/comunicacao/noticias/hu-ufsc-registra-avancos-em-2024#:~:text=Ainda%20em%202024%2C%20o%20HU,investimento%20na%20assist%C3%AAncia%20%C3%A0%20sa%C3%BAde. Data de acesso 12 dez. 2024.
CALZA, L. F.; NOGUEIRA, C. E. C.; SIQUEIRA, J. A. C. Diagnóstico preliminar e proposta de adequação para o uso eficiente da água no Hospital Universitário do Oeste do Paraná (Preliminary diagnosis and proposal of adequacy for the efficient use of water at the university hospital of the west of Paraná). Revista Acta Iguazu, Cascavel, v. 1, n. 4, p. 27–35, 2012.
DU, H.; ZHAO, Z.; XUE, H. ARIMA: a new model for daily water consumption prediction based on the autoregressive integrated moving average model and the Markov chain error correction. Water, v. 12, 2020. DOI: https://doi.org/10.3390/w12030760.
GONZÁLEZ, A. G.; GARCÍA-SANZ-CALCEDO, J.; SALGADO, D. R.; MENA, A. A quantitative analysis of cold water for human consumption in hospitals in Spain. International Journal of Environmental Research and Public Health, Basel, v. 13, n. 7, p. 647, 2016. Disponível em: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5058574/. Acesso em: 18 jul. 2025.
HYNDMAN, R. J.; KHANDAKAR, Y. Automatic time series forecasting: the forecast package for R. Journal of Statistical Software, v. 27, n. 3, p. 1–22, 2008. DOI: https://doi.org/10.18637/jss.v027.i03.
PIRES, C.; MARTINS, M. V. Enhancing water management: a comparative analysis of time series prediction models for distributed water flow in supply networks. Water, v. 16, n. 13, p. 1827, 2024. DOI: https://doi.org/10.3390/w16131827.
R CORE TEAM. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2025. Disponível em: <https://www.R-project.org/>. Acesso em: 21 jul. 2025.
RAJBALLIE, A.; TRIPATHI, V.; CHINCHAMEE, A. Water consumption forecasting models – a case study in Trinidad (Trinidad and Tobago). Water Supply, v. 22, 2022. DOI: https://doi.org/10.2166/ws.2022.147.
RISTOW, D. C. M.; HENNING, E.; KALBUSCH, A.; PETERSEN, C. E. Models for forecasting water demand using time series analysis: a case study in Southern Brazil. Journal of Water, Sanitation and Hygiene for Development, v. 11, n. 2, p. 231–240, 1 mar. 2021. DOI: https://doi.org/10.2166/washdev.2021.208.
SANTANA, L.; SANT’ANA, D. Análise dos usos finais de água em hospital público de Brasília, Brasil. Engenharia Sanitária e Ambiental, v, 28, e2022022, 2023. https://doi.org/10.1590/S1413-415220220228.
SETIC-UFSC. Coordenadoria de Gestão Ambiental – CGA. Disponível em: <https://gestaoambiental.ufsc.br/gestao-das-aguas/abastecimento-de-agua/>. Acesso em: 6 jun. 2025.
SILVA, A. C. DA, SILVA, F. DAS G. B. DA, VALÉRIO, V. E. DE M., SILVA, A. T. Y. L., MARQUES, S. M., & REIS, J. A. T. Application of data prediction models in a real water supply network: comparison between ARIMA and artificial neural networks. Revista Brasileira de Recursos Hídricos (RBRH), v. 29, 2024. DOI: https://doi.org/10.1590/2318-0331.292420230057.
