INTELLIGENT KNOWLEDGE DISCOVERY ON BUILDING ENERGY AND INDOOR CLIMATE DATA, ACTA UNIVERSITATIS OULUENSIS C Technica 587
|Kustantaja:||Oulun yliopisto|| |
|Painos:||Osajulkaisuväitöskirjan yhteenveto-osa|| |
|Sijainti:||Print Tietotalo|| |
|Tekijät:||RAATIKAINEN MIKA|| |
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.