Foto: Frontansicht des Gemeindezentrums Ludesch

CoolAIR - Predictive control of natural nighttime ventilation and daylight-optimized shading for passive building cooling

Natural nighttime ventilation and daylight-optimized shadowing are high potential approaches to efficiently and economical cool buildings. Nevertheless, the full potential cannot be acquired, since, if at all, such behavior is manually initiated by users. Goal of this project is the development of an automated, self-learning system that can assess the full cooling capabilities and establish an alternative to conventional air conditioning systems.

Short Description

Status

ongoing

Summary

Starting point / motivation

Whereas in the past overheating of rooms has been relevant only for limited hot periods during summer time, nowadays the problem is increasingly occurring also during transitional seasons. As a result the need for cooling solutions as well as the energy consumed by ventilation and air conditioning systems is increasing.

Passive measures such as natural nighttime ventilation and daylight-optimized shadowing are high potential approaches to efficiently and economical cool buildings. In particular, combining the two technologies allows to increase this potential. However, in practical implementations there are certain limitations preventing a full utilization.

In case cooling is to be realized purely by means of window ventilation, additional difficulties arise during planning, installation and operation: During design phase multiple parameters such as buoyancy forces or cross-ventilation are neglected in simple calculation guidelines or require individual complex building simulations to be determined.

Standard time or temperature-based control strategies are either unable to utilize the full potential or violating fundamental comfort levels. Innovative approaches such as predictive control facilitating weather forecasts or controls aggregating building zones improve the efficiency, yet require complex, central building automation control systems (BACS) and networks connecting sensors and actuators.

Limited scalability, high engineering efforts and complex configuration are immanent in these solutions. In particular, retrofitting and implementation in historic and listed buildings is economically hardly worthwhile.

Contents and goals

The project CoolAIR pursues a plug & play approach for room temperature regulation by means of an autonomously configuring, model-based predictive and combined control of natural nighttime ventilation and daylight-optimized shadowing. Ventilation is hereby assured simply by using and partially automating already existing ventilation openings such as windows, smoke outlets, door slits or air ducts. At the same time, engineering and installation efforts are reduced.

The novelty of this approach is a room centric design founded on a self-adapting model-based predictive control algorithm. By including self-learning capabilities the model automatically adapts to specific conditions of the room ranging from individual room geometries to effects of locally occurring heat islands or different thermal characteristics of the building (zone).

Due to the room-centric model-based control only a minimum of local sensors preferable located at the window are required and no building automation network needs to be installed. In addition, new methods are developed to estimate the cooling potential of window ventilation and daylight-optimized shadowing.

Methods

The decision-making algorithms of the predictive control are developed on the basis of extensive CFD and thermodynamic building simulations. The validation of these simulation models is carried out by a comprehensive monitoring of naturally cooled building zones.

For this purpose different single-space situations involving staircases and hallways are examined within the heritage-protected areas of the Danube University Krems. The validation of the developed control concept is carried out by a laboratory set-up in selected areas of the Danube University Krems as well as the New Castle in Vienna.

Expected results

The self-learning combined control of shadowing and window-based nighttime ventilation, coupled with the single-room control approach of CoolAIR is an extremely scalable, resource-friendly solution that prevents overheating of individual rooms up to entire buildings or building sections while increasing the comfort for the occupants.

The components developed within the CoolAIR project should give a proof of principle and assess the full cooling potential in the field test facilities. The will provide valuable information about about cooling potentials, application scenarios and boundary condition especially within historic and listed buildings.

Project Partners

Project management

  • Danube University Krems, Department for Building and Environment, Center for Climate Engineering
  • Danube University Krems, Department for Health Sciences and Biomedicine, Center for Integrated Sensor Systems

Project or cooperation partners

  • Forschung Burgenland GmbH
  • Johann Gerstmann
  • Woschitz Engineering ZT GmbH
  • Fürstner RWA Systeme und Technik GmbH
  • Zach Antriebe GmbH

Contact Address

DI Dr.techn. Daniela Trauninger
Dr.-Karl-Dorrek-Straße 30
A-3500 Krems
Tel.: +43 (2732) 893 - 2774
E-mail: daniela.trauninger@donau-uni.ac.at
Web: www.donau-uni.ac.at

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