On ventilation needs - towards demand controlled ventilation in dwellings.

Ventilation needs in dwellings must be determined on the basis of both requirements to theindoor air quality and necessary control of moisture conditions. As a first step towardsdevelopment of energy efficient ventilation strategies for demand controlled ventilation infuture dwellings theoretical analyses comprising a literature study and mathematicalsimulations have been carried out.

Demand controlled ventilation in schools - energetic and hygienic aspects

In this study, we investigated the indoor air quality (IAQ) in classrooms with exhaustventilation systems and in naturally ventilated classrooms. In the latter, we found peak CO2-concentrations of more than 4000 ppm. 1500 ppm was exceeded during 40 to 86% ofteaching time, dependent on class size. The windows were opened rarely in winter which ledto low mean air exchange rates of 0.20 0.23 h^-1. The operation of mechanical ventilationsystems improved IAQ considerably. Peak CO2-concentrations decreased to less than 2500ppm. 1500 ppm was exceeded for only 7 to 57% of teaching time.

Technical synthesis report: a summary of IEA ECBCS Annex 18 - Demand controlled ventilating systems.

The purpose of this report is to summarise the work of IEA Annex 18 on demand controlled ventilation. It is primarily aimed at building services practitioners, designers and policy makers who require background knowledge of the operational principles and range of applicability of this approach to ventilation. The primary focus is on applications and the conditions required for the operation of such systems. This international activity has been carried out by a working group of researchers from ten countries (Appendix 1) with Sweden bearing the main responsibility as Operating Agent.

The use of a mixed gas sensor in the study of indoor air quality and its application to demand based ventilation.

Demand Based Ventilation systems are potentially valuable in terms of energy saving in building with fluctuating occupation patterns. Most demand based ventilation systems are controlled by C02 measurement. However this approach cannot take account of other polluting elements found in indoor air. This paper will describe the results of a study of the indoor air quality in a recently built university library with continuous ventilation. The literature relating to typical levels of naturally occurring gases, volatile organic compounds and microbes, in indoor air is considered.

Development of a demand control strategy in buildings using radon and carbon dioxide levels.

Air change rates, indoor radon and carbon dioxide levels were monitored in a lecture theatre in the Hong Kong University of Science and Technology. Two preliminary measurements (Cases 1 and 2) and one series of demand control ventilation simulation (Case 3) were made to investigate the indoor air quality of the lecture theatre. Radon and carbon dioxide levels were found to be relatively high in Case 1 and later improved at the expense of operating the system catering for maximum occupancy in Case 2.

Sensor-based demand-controlled ventilation: a review.

With sensor-based demand-controlled ventilation ( SBDCV), the rate of ventilation is modulated over time based on the signals from indoor air pollutant or occupancy sensors. SBDCV offers two potential advantages: better control of indoor pollutant concentrations, mid lower energy use and peak energy demand.

Demand-Controlled Ventilation - Requirements and Control Strategies

Most standards for air handling systems prescribe a minimum air flow rate per person per hour based on full occupancy of the ventilated space. The number of occupants may fluctuate widely, however, and demand-controlled ventilation (DCV) responds to the actual demand for air renewal. There are now sensors capable of detecting this demand, and these are a prerequisite for DCV and good air quality. Key features of DCV are the incorporation of thermal tolerance bands (heating/cooling, humidification/dehumidification), and special control strategies to reduce or even disable the air flow rate.

Development of intelligent algorithms for indoor air quality control through natural ventilation strategies.

Simulations have been performed to investigate the performance of intelligent algorithms for control of indoor air quality through natural ventilation strategies whilst simultaneously meeting the requirements of thermal and visual comfort. The proposed control algorithms are founded on the knowledge base of the building physics and support the control of natural ventilation through control of the window opening, whilst simultaneously controlling the lighting, heating and cooling systems of the building.