Naslov
Algorithm for constructional characteristics data cleansing of large-scale public buildings database

Autori
Krstic, Hrvoje ; Teni, Mihaela

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
High Performance and Optimum Design of Structures and Materials III

Urednik/ci
De Wilde, Willy Patrick ; Hernandez, Santiago ; Karavanja, Stojan

Izdavač
Wessex Institute of Technology Press

Grad
Southampton

Godina
2018

Raspon stranica
213-224

ISBN
978-1-78466-289-9

Ključne riječi
Energy management information system, public sector buildings database, building characteristics, building maintenance, building energy refurbishment

Sažetak
Research presented in this paper utilizes public-sector buildings database obtained from the Croatian Energy Management Information System (EMIS) which comprises over 3500 public sector buildings. EMIS provides a transparent oversight and control of energy consumption, making itself an inevitable tool for systematic energy management. The EMIS database holds static technical data of each facility, including general, constructional data, energy performance data and dynamic energy usage data. But there are a lot of variables in a database with data values that are impossible, i.e. have values that are not logical or outside of possible, acceptable range, and they are probable the consequence of user input errors. Beside this there are also cases with missing data. Previously stated raises question - is it possible to make an algorithm for data cleansing and find a way to calculate the missing data? To use obtained database for further, more complex, analysis like clustering, machine learning and neural network applications, it is necessary to remove extreme values from the database. Research presented in this paper deals with this problem with an emphasis on buildings constructional characteristics and proposes cleansing algorithm. As a result possible range of variables and procedure for replacement of invalid input values is proposed. Research results and findings can be used in similar buildings databases to optimize dataset and exclude variables values with extreme values which can significantly impact modelling process. Further, the proposed algorithm can be useful when making decisions for energy refurbishment and building maintenance since it eliminates cases from the database that have misleading data. Presented results show that in some cases there are more than 80% of missing or excluded data. Findings can also be implemented in EMIS or similar system to avoid further entering of unacceptable data values.

Izvorni jezik
Engleski

Znanstvena područja
Građevinarstvo

Napomena
This work has been fully supported by Croatian Science Foundation under Grant No. IP-2016-06- 8350 "Methodological Framework for Efficient Energy Management by Intelligent Data Analytics" (MERIDA).

POVEZANOST RADA