English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Occupancy data is an essential input parameter for building energy simulation and has a huge impact on the accuracy of building energy models. Current approaches of obtaining occupancy data usually involve sophisticated occupancy detection devices and algorithms, or timeconsuming manual counting work. However, with the advancement of information technology and network service, the amount of data has accumulated sharply. The growing popularity of mobile devices has made it possible to acquire location data within a specific region based on active positioning requests from end-users, offering a new approach to obtain and extract occupancy profiles for different buildings and areas. This research uses clustering methods to extract typical daily and weekly occupancy profiles based on big data of location. Based on clustering analysis, several descriptive indices are introduced to establish occupancy models for different types of buildings. Moreover, comparisons of occupancy profiles between location data and energy codes are conducted to demonstrate the significance of locationdata-based occupancy profiles.

An Approach For Obtaining And Extracting Occupancy Patterns In Buildings Based On Mobile Positioning Data