Energy Demand For Ventilation Research Articles

Numerical analysis, design and application of a decentralized ventilation system with a heat recovery unit adapting to Nordic climates: A case study in Norway

This research investigated the energy efficiency of decentralized ventilation (DV) systems in cold weather conditions in comparison to centralized ventilation (CV) systems, focusing on three locations in Norway. The study found that DV systems with higher heat recovery unit (HRU) efficiency consumed less thermal and fan energy due to shorter air distribution pathways, utilizing a hybrid natural ventilation strategy in mild climates, and simpler space zoning for ventilation. This study proposes to determine a suitable ventilation system for adapting to cold climate. These methodologies are presented in three major parts: 1) ventilation energy demands of DV and CV systems in Norway, 2) performance of the heat recovery unit in the DV system, and 3) numerical analysis of the total ventilation energy demand of the DV system in cold climates using occupant ratio and existing measured data.The results showed specific time periods when it is ideal to use a fan-assisted NV system in cold climates, and in local conditions, 4–20% per year could benefit from a fan-assisted NV system using DV technology. A DV system with 0.7 HRU efficiency can save up to 14.5% of total HVAC energy compared to a CV system. The HRU heating efficiency played a crucial role in thermal ventilation energy demands in cold weather, while the cooling efficiency had no significant effect on energy demands. However, if a hydronic heating and cooling coil is added to the DV system, it consumes more than 9.08% of energy than using a pump. The energy-saving performance of the DV system decreases significantly if its HRU efficiency is below 50%, and there is no real advantage of using the DV system over the CV system in such cases. Nonetheless, the DV system can be utilized in various working conditions to conserve energy, such as different occupancy ratios, individual room space zoning, and indoor air pollution rates.

Numerical Analysis of the Energy Consumption of Ventilation Processes in the School Swimming Pool

Ventilation of an indoor swimming pool is a very energy consuming process. This is a result of, among other things the required high value of the ventilation air volume flow rate, calculated on the basis of the moisture gains in the facility. The total energy consumption consists of the heat required to heat this air and the electricity needed to transport it. It is possible to reduce the ventilation air volume flow rate by assuming the correct value of specific humidity of the supply and indoor air, but then a deterioration of thermal-moisture conditions in the building can be expected. The aim of this paper was to examine how the reduction of the supply air volume flow rate affects the energy consumption for indoor swimming pool ventilation. It was also checked how this consumption can be reduced by using two-stage heat recovery in the air handling unit. Multi-variant simulations of energy consumption for indoor swimming pool ventilation were carried out using the IDA ICE software for day and night operation of the swimming pool throughout the year. The results of the research proved that reduction of the supply air volume flow rate resulted in the lower energy expenditure on ventilation. The variant with additional local air supply to the lifeguard zone was also analysed, which caused only a slight increase in energy demand for ventilation.

Multi-objective optimization of a time-delay compensated ventilation control system in a subway facility – A harmony search strategy

Meeting the high current demand for public mobility requires massive transportation systems with their corresponding infrastructures. An example of this is the subway transportation system with underground public commuting space. However, indoor air quality (IAQ) management in this space is challenging due to these environments' hostile conditions with high ventilation energy demand. This study introduces an adaptive ventilation control system that dynamically regulates the setpoint of the coarse particulate matter (PM10) concentration considering the IAQ level and the ventilation energy consumption through a multi-objective harmony search (MOHS) strategy. Additionally, a time-delay term affects the IAQ model in the subway station. Therefore, a time-delay compensator (TDC), i.e., the Smith predictor (S–P), was introduced into the feedback loop to address this dead-time response. The proposed ventilation control system was compared with two other systems. This comparison is conducted under different outdoor air quality (OAQ) conditions, i.e., good, moderate, and unhealthy. The proposed ventilation control system (SP-VS) yielded an energy-saving potential in the range of 4%–13% under good OAQ conditions compared to the conventional ventilation system. In contrast, the health risk level for the unhealthy OAQ case decreased from an unhealthy to a moderate health risk level.

Imputing missing indoor air quality data via variational convolutional autoencoders: Implications for ventilation management of subway metro systems

Missing data represents a common problem in environmental and building-related processes, especially in the indoor air quality (IAQ) system of subway stations, where the collected information leads to actions in ventilation management. For these reasons, imputation approaches have been used to avoid information loss due to downsampling or sensor malfunction. This paper introduces an imputation approach for IAQ data via variational autoencoders (VAE) coupled with convolutional layers (VAE-CNN). Two scenarios were introduced: first, the IAQ dataset was corrupted by removing data intervals at different missing rates (i.e., 20%, 50%, and 80%), and second, a point-to-point removal of three sensors was conducted. The performance of the proposed method was compared with different techniques, showing that the VAE-CNN was superior to other methods even for massive amounts of missing data. Finally, the effects of missing and imputed data on the IAQ system in the D-subway station were evaluated in terms of ventilation energy demand, CO2 emissions, and IAQ level. The IAQ management with the imputed data could decrease by approximately 20% of CO2 emissions by reducing the energy demand, while the IAQ level increased by 3% in another scenario.

From Entropy Generation to Exergy Efficiency at Varying Reference Environment Temperature: Case Study of an Air Handling Unit

The continuous energy transformation processes in heating, ventilation, and air conditioning systems of buildings are responsible for 36% of global final energy consumption. Tighter thermal insulation requirements for buildings have significantly reduced heat transfer losses. Unfortunately, this has little effect on energy demand for ventilation. On the basis of the First and the Second Law of Thermodynamics, the concepts of entropy and exergy are applied to the analysis of ventilation air handling unit (AHU) with a heat pump, in this paper. This study aims to develop a consistent approach for this purpose, taking into account the variations of reference temperature and temperatures of working fluids. An analytical investigation on entropy generation and exergy analysis are used, when exergy is determined by calculating coenthalpies and evaluating exergy flows and their directions. The results show that each component of the AHU has its individual character of generated entropy, destroyed exergy, and exergy efficiency variation. However, the evaporator of the heat pump and fans have unabated quantities of exergy destruction. The exergy efficiency of AHU decreases from 45–55% to 12–15% when outdoor air temperature is within the range of −30 to +10 °C, respectively. This helps to determine the conditions and components of improving the exergy efficiency of the AHU at variable real-world local climate conditions. The presented methodological approach could be used in the dynamic modelling software and contribute to a wider application of the Second Law of Thermodynamics in practice.

Transition from n50 to actual air exchange dependent on climatic conditions

The knowledge of the air flow and air exchange in the building is critical both on the design and operation stage of the building. Infiltration of air interferes with the mechanical ventilation and determines the proper functioning of the natural ventilation system, still commonly used in the standard buildings. The building airtightness can be described by n50 parameter, however it does not specify the real air exchange in natural conditions. According to the simple procedure of the standard EN ISO 13789, factor n50 may be easily converted to the monthly averaged air change rate. However, it is difficult to accept the same value of air change rate in any month of a year, as it is often done in the certification procedures. More precise, climate dependent conversion procedures have been elaborated in USA, but they were developed for the specific local building technology and local climate conditions. This paper presents the results of the preliminary measurements conducted in a single family house in Poland, built in a heavy-weight technology. The real air exchange rate was measured in various climatic conditions by means of gas tracing method, with CO2 as the tracer gas, in order to prove a relationship between the enhanced procedure and the external conditions. Acceptable agreement between the results of the measurement and model calculations was obtained. Based on the preliminary results, the authors determined the more realistic influence of the enhanced algorithm on the ventilation energy demand. The use of the simplified model resulted in case of the analyzed object in 15% overestimation of the ventilation thermal losses.

On-site diagnosis of hybrid ventilation system in a renovated single-family house

There are approx. 5.5 million residential buildings in Poland including 5 million single-family houses, 90% of which were built before 2002. Most of the existing single-family houses are not energy-efficient and a large amount of energy is wasted. It is therefore essential to develop, experimentally validate and then disseminate strategies for thermal modernization of the existing single-family houses, including strategies for ventilation systems renovation. In two-storey single-family houses with sloping roofs it is possible to locate a ventilation heat recovery (VHR) unit and ventilation ducts in the unheated roof space. The paper presents the results of tests of combined ventilation systems: mechanical ventilation with heat recovery in rooms on the second floor and stack ventilation in the rooms on the first floor. On-site measurement of the efficiency of ventilation heat recovery was performed, the airtightness of the house was measured twice before and after its improvement. The results of the experimental tests of the hybrid system were used to estimate the ventilation airflow in the house and the energy demand for ventilation. The ventilation airflow was calculated using the software CONTAM. The estimated heat demand for the tested ventilation system was 37.4kWh/m2/season; though it seems possible to reduce it below 20kWh/m2/season.

In search for modeling predictive control of indoor air quality and ventilation energy demand in subway station

Indoor air quality (IAQ) in underground subway systems is highly influenced by outdoor air pollutants which enter platforms or tunnels through ventilation systems. To eliminate the nonlinear effect of outdoor air quality (OAQ) on IAQ, a predictive model of the neural network and dynamic external analysis is proposed with experimental measuring technical characterization. The partial least squares (PLS) method is used to predict both the particulate matter (PM) concentration and the energy usage of the ventilation system in the underground station. The developed predictive model of ventilation system operation at the D-subway station assured that the outdoor air pollutants do not influence IAQ, since the developed predictive model can remove the effects of OAQ on IAQ in platform as well as the energy used by the ventilation system. A comparison of the results with other prediction models shows that the proposed predictive model can decrease 20% prediction error of PM concentration in platform and also 64% prediction error of energy demand of the ventilation system. These findings suggest that ventilation control strategies should take into account the effects of outdoor air pollutants on IAQ prediction and energy demand.