Energy-saving feature extraction method for urban buildings with near-zero energy-consuming based on SVR

Xiaoliang Li and Jinfeng Lu

International Journal of Global Energy Issues, 2020, vol. 42, issue 5/6, 375-392

Abstract: In order to solve the problem of poor fitting performance of traditional energy saving feature extraction method in urban buildings with near zero energy consumption, an energy saving feature extraction method based on SVR is proposed. The data are recovered and processed by means of mean value substitution method, and the correlation order of data parameters is realised through grey correlation analysis. Based on the feature weighting theory, the energy saving data of near zero energy saving buildings are cluster analysed. The main component analysis method is used to deal with feature extraction data, reduce the size of feature extraction data, and use SVR to achieve the extraction of energy saving characteristics of nearly zero energy consumption buildings in cities. The experimental results show that the method is always higher than other methods, with a maximum of 88%. The results show that the method is effective in feature extraction.

Keywords: SVR; near-zero energy-consuming buildings in cities; energy-saving feature extraction; outlier point processing; abnormal data detection. (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=111185 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijgeni:v:42:y:2020:i:5/6:p:375-392

Access Statistics for this article

More articles in International Journal of Global Energy Issues from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().