The operational practice of water distribution systems lacks the implementation of advanced tools for processing and analyzing monitored data. This is the case at many levels of water supply management, where measurements are recorded, most often creating uninterpretable data sets. With the arrival of data recording capabilities that can be described as high-frequency, there is a need for a simultaneous implementation of suitable data science techniques as the basis for smart water supply networks. To achieve the goals of implementing intelligence at the water meter level, it is necessary to allow measurement of water consumption with a precise measurement interval and advanced data analysis, which should result in effective inference and management of water distribution systems. This paper presents the results of the use of machine learning models to predict short-term water consumption for multifamily buildings. Linear models, simple neural network, nearest neighbour algorithm and decision trees were used to predict water consumption. The study evaluated features extracted from the water consumption waveforms and combinations of data sets given to the input of the regression model. It was also verified how the degree of data aggregation and the structure of the building influence the prediction error.
About Authors:
dr inż. Sandra Śmigiel – https://orcid.org/0000-0003-2459-5494 Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich, Wydział Inżynierii Mechanicznej, Bydgoszcz
dr inż. Justyna Stańczyk – https://orcid.org/0000-0002-5676-1787 Uniwersytet Przyrodniczy we Wrocławiu, Instytut Inżynierii Środowiska, Wrocław
mgr inż. Paulina Dzimińska – https://orcid.org/0000-0003-0427-5960 Miejskie Wodociągi i Kanalizacja w Bydgoszczy – sp. z o.o., Bydgoszcz dr inż. Damian Ledziński – https://orcid.org/0000-0003-0796-4390, dr hab. inż. Tomasz Andrysiak, prof. PBŚ – https://orcid.org/0000- 0001-8138-6619 Politechnika Bydgoska im. Jana i Jędrzeja Śniadeckich, Wydział Telekomunikacji, Informatyki i Elektrotechniki, Bydgoszcz prof. dr hab. inż. Paweł Licznar – https://orcid.org/0000-0002-2559-5296 Wydział Instalacji Budowlanych, Hydrotechniki i Inżynierii Środowiska, Politechnika Warszawska, Warszawa. Corresponding author: sandra.smigiel@pbs.edu.pl