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Autoregressiv analys på tidsseriedata från en kontorsbyggnad : Smarta byggnader i teori och praktik / Autoregressive analysis of time series data from an office building

The building sector is responsible for around 39% of the energy consumption in Sweden, and one way to work towards sustainable societies could be to make the buildings more energy efficient. One approach to make a building more energy efficient is to use knowledge gained from digitalization of the building and to make the building smart. This thesis aims to study the area of smart buildings, and the ongoing work with smart solutions within the real estate sector.  Two parallel investigations are used to study the area. One is an interview study in order to map the ongoing work with smart buildings. The situation on the market, the matureness of technical solutions as well as ongoing trends and challenges are amongst other things studied. The second investigation consists of a pilot project which aims to exemplify how time series data analysis could be used in order to make a building smarter. Time-series prediction provides a way to discover and quantify regularities in such data, and methods of time series prediction point to how to make building management more efficient.  The result of the study shows that the smart building market is not yet stabilized, but that the interest in working with smart buildings is big. There are many smaller solutions which are being tested and implemented, but there is no consensus of what the definition of a smart building really is. The results of the data analysis indicate two results, firstly, it provides insight in the data, and reports how one should prepare the data for subsequent analysis, and secondly we report results for different autoregressive (AR)-based time series models. For the second result, we indicate how methods of K-means improve over linear AR-based modelling, pointing to the possible use of nonlinear modelling. We however question whether performance improvements are sufficiently large for this application to justify the additional computational demands.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-414154
Date January 2020
CreatorsGrönlund, Clara, Gustafsson, Astrid
PublisherUppsala universitet, Avdelningen för systemteknik, Uppsala universitet, Avdelningen för systemteknik
Source SetsDiVA Archive at Upsalla University
LanguageSwedish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess
RelationUPTEC STS, 1650-8319 ; 20017

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