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Modelling Indoor Air Quality Using Sensor Data and Machine Learning Methods

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dc.date.accessioned 2021-03-30T12:52:07Z
dc.date.available 2021-03-30T12:52:07Z
dc.date.issued 2021-03-30
dc.identifier.uri http://hdl.handle.net/123456789/34951
dc.title Modelling Indoor Air Quality Using Sensor Data and Machine Learning Methods en
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI http://data.hulib.helsinki.fi/id/8d59209f-6614-4edd-9744-1ebdaf1d13ca
ethesis.university.URI http://data.hulib.helsinki.fi/id/50ae46d8-7ba9-4821-877c-c994c78b0d97
ethesis.university Helsingin yliopisto fi
ethesis.university University of Helsinki en
ethesis.university Helsingfors universitet sv
dct.creator Muiruri, Dennis
dct.issued 2021
dct.language.ISO639-2 eng
dct.abstract Ubiquitous sensing is transforming our societies and how we interact with our surrounding envi- ronment; sensors provide large streams of data while machine learning techniques and artificial intelligence provide the tools needed to generate insights from the data. These developments have taken place in almost every industry sector with topics such as smart cities and smart buildings becoming key topical issues as societies seek more sustainable ways of living. Smart buildings are the main context of this thesis. These are buildings equipped with various sensors used to collect data from the surrounding environment allowing the building to adapt itself and increasing its operational efficiency. Previously, most efforts in realizing smart buildings have focused on energy management and au- tomation where the goal is to improve costs associated with heating, ventilation, and air condi- tioning. A less studied area involves smart buildings and their indoor environments especially relative to sub-spaces within a building. Increased developments in low-cost sensor technologies have created new opportunities to sense indoor environments in more granular ways that provide new possibilities to model finer attributes of spaces within a building. This thesis focuses on modeling indoor environment data obtained from a multipurpose building that serves primarily as a school. The aim is to explore the quality of the indoor environment relative to regulatory guidelines and also exploring suitable predictive models for thermal comfort and indoor air quality. Additionally, design science methodology is applied in the creation of a proof of concept software system. This system is aimed at demonstrating the use of Web APIs to provide sensor data to clients that may use the data to render analytics among other insights to a building’s stakeholders. Overall, the main technical contributions of this thesis are twofold: (i) a potential web-application design for indoor air quality IoT data and (ii) an exposition of modeling of indoor air quality data based on a variety of sensors and multiple spaces within the same building. Results indicate a software-based tool that supports monitoring the indoor environment of a building would be beneficial in maintaining the correct levels of various indoor parameters. Further, modeling data from different spaces within the building shows a need for heterogeneous models to predict variables in these spaces. This implies parameters used to predict thermal comfort and air quality are different in varying spaces especially where the spaces differ in size, indoor climate control settings, and other attributes such as occupancy control. en
dct.subject IoT
dct.subject Sensors
dct.subject Smart Buildings
dct.subject Indoor Air Quality
dct.subject Predicting
dct.subject Machine Learning
dct.language en
ethesis.isPublicationLicenseAccepted true
ethesis.language.URI http://data.hulib.helsinki.fi/id/languages/eng
ethesis.language englanti fi
ethesis.language English en
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
ethesis.thesistype.URI http://data.hulib.helsinki.fi/id/thesistypes/mastersthesis
dct.identifier.ethesis E-thesisID:128fdfed-228b-4594-be41-b9d335148abb
dct.identifier.urn URN:NBN:fi:hulib-202103301762
dc.type.dcmitype Text
ethesis.facultystudyline Mathematics / Computer and data science / Physics / Chemistry fi
ethesis.facultystudyline Mathematics / Computer and data science / Physics / Chemistry en
ethesis.facultystudyline Mathematics / Computer and data science / Physics / Chemistry sv
ethesis.facultystudyline.URI http://data.hulib.helsinki.fi/id/SH50_147
ethesis.mastersdegreeprogram Datatieteen maisteriohjelma fi
ethesis.mastersdegreeprogram Master's Programme in Data Science en
ethesis.mastersdegreeprogram Magisterprogrammet i data science sv
ethesis.mastersdegreeprogram.URI http://data.hulib.helsinki.fi/id/MH50_010

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