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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we propose a new end-to-end methodology to optimize the energyperformance as well as comfort and air quality in large buildings without anyrenovation work. We introduce a metamodel based on recurrent neural networksand trained to predict the behavior of a general class of buildings using adatabase sampled from a simulation program. This metamodel is then deployed indifferent frameworks and its parameters are calibrated using the specific dataof two real buildings. Parameters are estimated by comparing the predictions ofthe metamodel with real data obtained from sensors using the CMA-ES algorithm,a derivative free optimization procedure. Then, energy consumptions areoptimized while maintaining a target thermal comfort and air quality, using theNSGA-II multi-objective optimization procedure. The numerical experimentsillustrate how this metamodel ensures a significant gain in energy efficiency,up to almost 10%, while being computationally much more appealing thannumerical models and flexible enough to be adapted to several types ofbuildings.

End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort