Water demand forecasting using time series
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/33797 |
Resumo: | Process optimization has been an area of growing interest for companies. For a water supply management company, water distribution optimization means coordinating efficiently the water harvesting, treatment and distribution stages in a single process. However, to plan efficiently the operation of water supply systems it is mandatory to forecast the water demands. The forecasting task is possible if some variables are previously known, such as the rainy days that directly influence the harvest stage. As well as weather conditions, there are other variables that affect the success of a good water demand forecast, such as holidays. The large number of variables requires increasingly sophisticated forecasting models that require greater information processing capacity. These models do not always find acceptable solution, and, in practice, the variables needed to train a robust model are not always available. This difficulty is overcome by the implementation of forecast models based solely on the time-series. These are computationally simpler and do not require data from entities outside the Water Supply companies. The focus of this work is to analyze, implement and test forecasting algorithms based on time series applied to water demand, having as input variables only the history of water consumption. The first stage of this work consisted of studying the history of water consumption in the Penacova area to trace temporal patterns in the data. The second stage deals with the implementation of a classical ARIMA prediction model and implementation of a Bakker heuristic model. Both models were compared and different advantages and disadvantages were analyzed. |
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Water demand forecasting using time seriesWater supply system (WSS)Water demand forecastingTime seriesForecasting methodsARIMAHeuristic forecasting modelProcess optimization has been an area of growing interest for companies. For a water supply management company, water distribution optimization means coordinating efficiently the water harvesting, treatment and distribution stages in a single process. However, to plan efficiently the operation of water supply systems it is mandatory to forecast the water demands. The forecasting task is possible if some variables are previously known, such as the rainy days that directly influence the harvest stage. As well as weather conditions, there are other variables that affect the success of a good water demand forecast, such as holidays. The large number of variables requires increasingly sophisticated forecasting models that require greater information processing capacity. These models do not always find acceptable solution, and, in practice, the variables needed to train a robust model are not always available. This difficulty is overcome by the implementation of forecast models based solely on the time-series. These are computationally simpler and do not require data from entities outside the Water Supply companies. The focus of this work is to analyze, implement and test forecasting algorithms based on time series applied to water demand, having as input variables only the history of water consumption. The first stage of this work consisted of studying the history of water consumption in the Penacova area to trace temporal patterns in the data. The second stage deals with the implementation of a classical ARIMA prediction model and implementation of a Bakker heuristic model. Both models were compared and different advantages and disadvantages were analyzed.A área de otimização dos processos tem vindo a ter um interesse crescente por parte das empresas. Para uma empresa de gestão de abastecimento de água, a otimização de distribuição de água significa coordenar, de forma eficiente, os processos de recolha, tratamento e distribuição de água num único processo. Contudo, para planear de forma eficiente o funcionamento dos sistemas de abastecimento de água é essencial prever as demandas de água. A tarefa de previsão é possível se algumas variáveis forem conhecidas previamente, como os dias de chuva que influenciam diretamente o processo de recolha de água. Tal como as condições meteorológicas, existem outras variáveis que afetam o sucesso de uma boa previsão de demanda de água, como os dias de férias. Um elevando número de variáveis requer modelos de previsão cada vez mais sofisticados que requerem maior capacidade de processamento de informação. Esses modelos nem sempre encontram uma solucão razoável e, na prática, as variáveis necessárias para treinar um modelo robusto nem sempre estão disponíveis. Essa dificuldade é superada com a implementação de modelos de previsão baseados somente em séries-temporais. São computacionalmente mais simples e não requerem dados de entidades externas às empresas de abastecimento de água. O foco deste trabalho é analisar, implementar e testar algoritmos de previsão baseados em séries temporais aplicadas à demanda de água, tendo como variável de entrada apenas o histórico de consumo de água. A primeira etapa deste trabalho consistiu em estudar o histórico do consumo de água na região de Penacova para traçar padrões temporais de consumo. A segunda etapa trata da implementação de um modelo clássico de previsão ARIMA e da implementação de um modelo heurístico de Bakker. Ambos os modelos foram comparados e diferentes vantagens e desvantagens foram analisadas.2022-05-04T09:02:10Z2021-12-10T00:00:00Z2021-12-10info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/33797engLysenko, Alinainfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T12:05:01Zoai:ria.ua.pt:10773/33797Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:05:08.586931Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Water demand forecasting using time series |
title |
Water demand forecasting using time series |
spellingShingle |
Water demand forecasting using time series Lysenko, Alina Water supply system (WSS) Water demand forecasting Time series Forecasting methods ARIMA Heuristic forecasting model |
title_short |
Water demand forecasting using time series |
title_full |
Water demand forecasting using time series |
title_fullStr |
Water demand forecasting using time series |
title_full_unstemmed |
Water demand forecasting using time series |
title_sort |
Water demand forecasting using time series |
author |
Lysenko, Alina |
author_facet |
Lysenko, Alina |
author_role |
author |
dc.contributor.author.fl_str_mv |
Lysenko, Alina |
dc.subject.por.fl_str_mv |
Water supply system (WSS) Water demand forecasting Time series Forecasting methods ARIMA Heuristic forecasting model |
topic |
Water supply system (WSS) Water demand forecasting Time series Forecasting methods ARIMA Heuristic forecasting model |
description |
Process optimization has been an area of growing interest for companies. For a water supply management company, water distribution optimization means coordinating efficiently the water harvesting, treatment and distribution stages in a single process. However, to plan efficiently the operation of water supply systems it is mandatory to forecast the water demands. The forecasting task is possible if some variables are previously known, such as the rainy days that directly influence the harvest stage. As well as weather conditions, there are other variables that affect the success of a good water demand forecast, such as holidays. The large number of variables requires increasingly sophisticated forecasting models that require greater information processing capacity. These models do not always find acceptable solution, and, in practice, the variables needed to train a robust model are not always available. This difficulty is overcome by the implementation of forecast models based solely on the time-series. These are computationally simpler and do not require data from entities outside the Water Supply companies. The focus of this work is to analyze, implement and test forecasting algorithms based on time series applied to water demand, having as input variables only the history of water consumption. The first stage of this work consisted of studying the history of water consumption in the Penacova area to trace temporal patterns in the data. The second stage deals with the implementation of a classical ARIMA prediction model and implementation of a Bakker heuristic model. Both models were compared and different advantages and disadvantages were analyzed. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-12-10T00:00:00Z 2021-12-10 2022-05-04T09:02:10Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/33797 |
url |
http://hdl.handle.net/10773/33797 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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