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Oulun yliopiston väitöskirjat

INTELLIGENT KNOWLEDGE DISCOVERY ON BUILDING ENERGY AND INDOOR CLIMATE DATA, ACTA UNIVERSITATIS OULUENSIS C Technica 587

ISBN-13:	978-952-62-1379-8
Kieli:	englanti
Kustantaja:	Oulun yliopisto
Oppiaine:	Tekniikka
Painos:	Osajulkaisuväitöskirjan yhteenveto-osa
Painosvuosi:	2016
Sijainti:	Print Tietotalo
Sivumäärä:	112
Tekijät:	RAATIKAINEN MIKA

27.50 €

A future vision of enabling technologies for the needs of energy conservation as well as energy efficiency based on the most important megatrends identified, namely climate change, urbanization, and digitalization. In the United States and in the European Union, about 40% of total energy consumption goes into energy use by buildings. Moreover, indoor climate quality is recognized as a distinct health hazard. On account of these two factors, energy efficiency and healthy housing are active topics in international research. The main aims of this thesis are to study which elements affect indoor climate quality, how energy consumption describes building energy efficiency and to analyse the measured data using intelligent computational methods. The data acquisition technology used in the studies relies heavily on smart metering technologies based on Building Automation Systems (BAS), big data and the Internet of Things (IoT). The data refining process presented and used is called Knowledge Discovery in Databases (KDD). It contains methods for data acquisition, pre-processing, data mining, visualisation and interpretation of results, and transformation into knowledge and new information for end users. In this thesis, four examples of data analysis and knowledge deployment concerning small houses and school buildings are presented. The results of the case studies show that the data mining methods used in building energy efficiency and indoor climate quality analysis have a great potential for processing a large amount of multivariate data effectively. An innovative use of computational methods provides a good basis for researching and developing new information services. In the KDD process, researchers should co-operate with end users, such as building management and maintenance personnel as well as residents, to achieve better analysis results, easier interpretation and correct conclusions for exploiting the knowledge.

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