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Maskininlärning i fastighetsbranschen: Prediktion av felanmälningar gällande inomhusklimat baserat på sensordata
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2017 (Swedish)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesisAlternative title
Machine learning in the real estate industry : Predictions of error reportings regarding indoor climate based on sensor data (English)
Abstract [en]

This thesis investigates the prerequisites needed for the Swedish real estate company Fabege to create useful machine learning models for classification and prediction of error reports from tenants. These error reports are regarding cold indoor climates and bad indoor air quality. By analyzing the available data, that consists of error reporting data, weather data and indoor climate data, the thesis investigates the different correlations between the sensor data and the error reports. By using an algorithm called decision jungle, two machine learning models have been trained in Microsoft Azure Machine Learning Studio. The main model, trained on error reporting data and weather data, shows the possibilities to classify data instances as a part of different error reporting classes. The model proves that it is possible to predict the emergence of future error reports of different classes with an average accuracy of 78%. The complementary model, trained on a small but more richly annotated dataset consisting of one year of indoor sensor data as well as the above-mentioned data, shows that there is a possibility to improve the main model by using indoor climate data.

The thesis has shown that for Fabege to expand and improve these models, the amount of data collected from the indoor sensors needs to be largely increased. Fabege also needs to improve the quality of the error reporting data, which could be achieved by improving the error reporting form used by the tenants.

Place, publisher, year, edition, pages
2017.
Series
UPTEC STS, ISSN 1650-8319 ; 17034
Keyword [en]
Classification, prediktion, real estate, algorithm, decision jungle, indoor climate, sensor data
Keyword [sv]
Maskininlärning, beslutsdjungel, klassificering, algoritm, sensordata, inomhusklimat, prediktion, fastigheter, Azure
National Category
Other Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-338579OAI: oai:DiVA.org:uu-338579DiVA, id: diva2:1172725
External cooperation
Business Vision Consulting AB
Educational program
Systems in Technology and Society Programme
Presentation
2017-12-18, Ångströmslaboratoriet, Lägerhyddsvägen 1, Uppsala, 14:15 (Swedish)
Supervisors
Examiners
Available from: 2018-01-17 Created: 2018-01-10 Last updated: 2018-01-17Bibliographically approved

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