Share

Development of climate classification through hierarchical clustering for building energy simulation.

Number: 3666

Author(s) : PERNIGOTTO G., GASPARELLA A., HENSEN J. L. M.

Summary

Climate classification plays an important role for the identification of homogeneous groups of climates, from which representative locations can be extracted and used for building energy simulation analyses. Nevertheless, according to the current state-of-the-art, the main reference systems consider just a fraction of those weather quantities which are relevant in the building energy balance, i.e., ambient temperature and humidity and solar radiation. To overcome this issue, in previous researches a new methodology was defined, based on monthly series of weather quantities, statistical analyses and data-mining techniques for climate clustering. In this work, with the aim of further developing such approach, a shorter time-discretization of weather quantities, i.e., a weekly discretization, was tested, alongside additional variables describing the daily range of ambient temperature and humidity. In order to investigate the potential of those modifications, a dataset with more than 300 European reference climates was analyzed and subdivided into climate classes according to the proposed clustering procedure.

Available documents

Format PDF

Pages: 10

Available

  • Public price

    20 €

  • Member price*

    15 €

* Best rate depending on membership category (see the detailed benefits of individual and corporate memberships).

Details

  • Original title: Development of climate classification through hierarchical clustering for building energy simulation.
  • Record ID : 30028666
  • Languages: English
  • Subject: Technology
  • Source: 2021 Purdue Conferences. 6th International High Performance Buildings Conference at Purdue.
  • Publication date: 2021/05/24
  • Document available for consultation in the library of the IIR headquarters only.

Links


See other articles from the proceedings (52)
See the conference proceedings

Indexing