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

Herein we report a multi-zone, heating, ventilation and air-conditioning(HVAC) control case study of an industrial plant responsible for cooling ahospital surgery center. The adopted approach to guaranteeing thermal comfortand reducing electrical energy consumption is based on a statisticalnon-parametric, non-linear regression technique named Gaussian processes. Ourstudy aimed at assessing the suitability of the aforementioned technique tolearning the building dynamics and yielding models for our model predictivecontrol (MPC) scheme. Experimental results gathered while the building wasunder regular use showcase the final controller performance while subject to anumber of measured and unmeasured disturbances. Finally, we provide readerswith practical details and recommendations on how to manage the computationalcomplexity of the on-line optimization problem and obtain high-qualitysolutions from solvers.

Experimental Data-Driven Model Predictive Control of a Hospital HVAC System During Regular Use