Demand-controlled Ventilation Research Articles

Simultaneous thermal zoning and demand control ventilation

Heating, Ventilation, and Air Conditioning systems play a crucial role in controlling indoor air conditions, including temperature, humidity, and CO₂ concentration, to ensure human comfort and safety. Thermal zoning and demand-controlled ventilation have demonstrated enhancements in comfort and energy efficiency. This study aims to develop a mathematical model for a Heating, Ventilation, and Air Conditioning system employing an estimated state-feedback control algorithm to optimize indoor comfort and energy savings while minimizing sensor requirements. The model encompasses dynamic equations governing CO₂ concentration and temperature in a five-room building. Temperature regulation is achieved by adjusting the airflow from a cooling unit with dampers, while CO₂ levels are managed by controlling the proportion of fresh air intake. To showcase the advantages of the proposed control approach, comparisons were made with alternative methods based on response performance, energy consumption, and sensor requirements. The estimated state-feedback control outperformed other approaches, requiring only one temperature and one CO₂ sensor at the mixed return. Additional occupancy sensors in each room are not necessary if an Extended Kalman filter is utilized. This technique exhibits scalability, adaptable to varying output variables and room configurations, and holds promise for broader implementation in the HVAC industry. By optimizing energy consumption and maintaining indoor air quality, this approach aligns with established sustainability metrics, promoting environmental responsibility. Compliance with health and safety audit standards ensures the fulfillment of social targets, while adherence to regulations and laws ensures governance targets are met.

Occupancy sensor-enabled demand control ventilation using virtual outdoor air flow meters in air handling units without an outdoor air flow meter

Nowadays, the outdoor air (OA) flow requirement for demand control ventilation (DCV) can be accurately determined using occupancy sensors in commercial buildings. However, in air handling units (AHUs) without precise OA flow measurements, the OA is often controlled by a fixed-damper position method, which cannot maintain the required OA flow rate. The objective of this study is to develop and evaluate occupancy sensor-enabled DCV OA control using a virtual OA flow meter in AHUs. Firstly, a virtual OA flow meter is derived from a virtual supply fan flow meter by applying the correlation between the OA ratio and damper position under appropriate building pressure control. Subsequently, experiments are conducted to calibrate the virtual OA flow meter in an AHU with the design OA flow rate of 120 L/s. Finally, occupancy sensor-enabled DCV OA control is implemented by using the calibrated virtual OA flow meter. The evaluation results show that the proposed DCV OA control can match the actual OA flow rate well with the required OA flow rate. The root mean squared error (RMSE) falls within the range of 16–20 L/s, demonstrating an improvement over the fixed-damper position control, which yields an RMSE of 36 L/s.

Indoor environmental quality in schools: NOTECH solution vs. standard solution.

In many Danish schools, the indoor environmental quality (IEQ) is challenged and studies document a poor IEQ in a majority of existing schools. Municipalities cannot afford comprehensive renovations and expensive mechanical ventilation solutions, hence public schools often suffer from poor indoor environment conditions. This study tests a new façade based, demand-controlled ventilation solution called NOTECH in the renovation of school. The study tests NOTECH vs. existing mechanical ventilation solution, comparing performance of both solutions at Skovbrynet Skole in Denmark. The project investigates the effect of the NOTECH solution in a primary school classroom, comparing it to a similar classroom with conventional, mechanical ventilation. Methodically, indoor environmental quality and energy performance is monitored in the two identical classrooms during one school year 2018 - 2019. The results show that both systems keep the conditions within acceptable limits and CO2 levels below 1000 ppm, which is the requirement according to the Danish Building Regulations. In terms of costs, the NOTECH system has a lower overall cost than the mechanical ventilation system, with total estimated costs for installation, heating, electricity and maintenance amounting to approximately 35% of the mechanical system's costs. Finally, the results show that the NOTECH solution has a smaller embedded CO2 footprint for building materials, reducing the estimated carbon load by 95% compared to the mechanical ventilation solution. While the performance of the both systems complies to the Danish Building Regulations, the indoor environmental quality between systems differs significantly. Results showing a higher air-temperature and lower relative air-humidity in the classroom with mechanical ventilation during winter and lower CO 2 levels in the mechanically ventilated classroom during winter and summer. Costs for implementation, energy consumption for heating and CO 2 footprint for building materials are significantly lower for the NOTECH solution, compared to the mechanical solution.

Ventilation retrofit design for existing primary and secondary school classrooms under epidemic conditions

In the context of regular epidemic prevention and control, most primary and secondary schools in developing countries are naturally ventilated. They are unable to use measures such as air filters equipped with centralized mechanical ventilation systems and demand-controlled ventilation. Therefore, the feasibility of using automatic control of negative pressure ventilation (NPV) in conjunction with CO2 concentration detectors to reduce the rate of infection in the classroom was investigated. Firstly, the CO2 concentration threshold calculation for epidemic prevention was carried out, and then the measured data of the classroom was fitted with the Computational Fluid Dynamics (CFD) simulation data to establish a simulation and analysis platform to comprehensively evaluate different NPV conditions. The results show that NPV can control the direction of airflow and CO2 concentration in the teaching room, which can significantly reduce the risk of infection, and that Upward Supply Upward Return NPV with automatic control of the CO2 concentration detector and the combination of windshields can protect the students from cross-infection. The study aims to optimize classroom ventilation strategies for epidemic proofing through low-cost building modifications, which could inform the retrofitting of hundreds of thousands of established primary and secondary schools in developing countries. Practical Application Ventilation systems controlled by environmental parameters have the advantages of low cost, high automation, and ease of operation. This system makes designing classrooms to combat airborne epidemics easier by implementing simple retrofitting measures for developing countries where natural ventilation is predominantly used and where most classrooms are not equipped with fresh air ducts. Tests of the top inlet and top return ventilation system proposed in this paper found that the classroom’s measured indoor carbon dioxide concentrations were reduced to below the theoretical limits.

Zonal demand-controlled ventilation strategy to minimize infection probability and energy consumption: A coordinated control based on occupant detection

Due to the outbreak of COVID-19, an increased risk of airborne transmission has been experienced in buildings, particularly in confined public places. The need for ventilation as a means of infection prevention has become more pronounced given that some basic precautions (like wearing masks) are no longer mandatory. However, ventilating the space as a whole (e.g., using a unified ventilation rate) may lead to situations where there is either insufficient or excessive ventilation in localized areas, potentially resulting in localized virus accumulation or large energy consumption. It is of urgent need to investigate real-time control of ventilation systems based on local demands of the occupants to strike a balance between infection risk and energy saving. In this work, a zonal demand-controlled ventilation (ZDCV) strategy was proposed to optimize the ventilation rates in sub-zones. A camera-based occupant detection method was developed to detect occupants (with eight possible locations in sub-zones denoted as ‘A’ to ‘H’). Linear ventilation model (LVM), dimension reduction, and artificial neural network (ANN) were integrated for rapid prediction of pollutant concentrations in sub-zones with the identified occupants and ventilation rates as inputs. Coordinated ventilation effects between sub-zones were optimized to improve infection prevention and energy savings. Results showed that rapid prediction models achieved an average prediction error of 6 ppm for CO2 concentration fields compared with the simulation under different occupant scenarios (i.e., occupant locations at ABH, ABCFH, and ABCDEFH). ZDCV largely reduced the infection risk to 2.8% while improved energy-saving efficiency by 34% compared with the system using constant ventilation rate. This work can contribute to the development of building environmental control systems in terms of pollutant removal, infection prevention, and energy sustainability.

Optimising Mechanical Ventilation for Indoor Air Quality and Thermal Comfort in a Mediterranean School Building

The growing concern over indoor air quality (IAQ) and thermal comfort in classrooms, especially post-COVID-19, underscores the critical need for optimal ventilation systems to bolster students’ health and academic performance. This study explores the potential for improving indoor air quality and thermal comfort in the most energy- and cost-optimal manner using a demand-controlled ventilation (DCV) system coupled with a carbon dioxide control sensor. This is achieved through precooling via night purging in summer and by introducing warmer corridor air into the classroom in winter. The methodology employs both computer simulation and a real-world case study. The findings reveal that while natural ventilation in winter can achieve IAQ standard (EN 16798-1) thresholds for classrooms under favourable outdoor conditions, it results in uncontrolled and excessive energy loss. The retrofitted DCV system, however, maintained CO2 levels below the recommended thresholds for at least 76% of the year depending on classroom orientation and only exceeded 1000 ppm for a maximum of 6% of the year. This study also indicates that utilising the external corridor as a sunspace can further enhance the system’s efficiency by preheating incoming air. This comprehensive study highlights the significant potential for integrating mechanical and passive solutions in school ventilation systems. This contributes to the attainment of the United Nations Sustainable Development Goal 11 and ensures healthier and more energy-efficient learning environments that benefit both students and the environment.

European residential ventilation: Investigating the impact on health and energy demand

The fundamental goal of building ventilation is to provide indoor conditions conducive to occupant health with low energy use. However, given the dynamic interplay of indoor and outdoor environmental conditions, there is limited knowledge on benchmarking human health and energy performance in relation to different ventilation systems. We coupled outdoor air pollution and meteorological data from 9 European cities with building energy simulations to investigate the effect of residential ventilation on indoor air quality, Disability-Adjusted Life Years (DALYs), and energy demand. Examining five representative ventilation systems, each operating under three distinct ventilative cooling (VC) scenarios, our findings reveal that PM2.5 accounted for the majority of DALYs (∼94 %). Filtering outdoor air in accordance with the standards reduced DALYs on average by ∼37 %. In the presence of filtration, VC based on indoor-outdoor temperatures increased DALYs by ∼10 %. However, a more comprehensive VC control based on both indoor-outdoor temperatures and outdoor air pollution did not increase DALYs. Overall, demand-controlled mechanical ventilation with energy recovery, outdoor air filtration, and VC resulted in the lowest energy demand and DALYs. Our results suggest that considering both indoor and outdoor environmental parameters can improve the performance of residential ventilation in terms of health and energy.

Effect of demand-controlled ventilation strategies on indoor air pollutants in a classroom: A Norwegian case study

The choice of the minimum ventilation rate (Vmin) in a demand-controlled ventilation strategy can influence energy demand but also introduce outdoor air pollutants. The latter may have direct health effects, as well as affect indoor chemical reactions. In this paper, we evaluate the effect of ventilation rates and operation hours on the level of CO2, nitrogen dioxide (NO2), and ozone (O3) in a classroom during normal use. We compared the baseline ventilation scenario (S0) with a Vmin of 430 m3/h with S1; Vmin of 150 m3/h for normal ventilation operation time (6:30-17:00) and continuous ventilation for 24h (S2). We found that S1 with reduced Vmin would lower the ozone concentration by 35% during the hours before occupancy compared to S0. Moreover, continuous ventilation during night time with a low Vmin resulted in almost as high O3 concentrations as the baseline ventilation scenario. As O3 reacts easily with certain VOCs to produce secondary organic aerosols, the level of Vmin and the ventilation duration would impact the indoor air quality upon entering the classroom.

Method of test for CO2-based demand control ventilation systems: Benchmarking the state-of-the-art and the undervalued potential of proportional-integral control

Carbon dioxide (CO2) based demand control ventilation (DCV) adjusts a building’s outdoor air ventilation rate in response to indoor CO2 concentration to save energy while maintaining indoor air quality. Packaged heating, ventilation, and air-conditioning systems often contain DCV controllers with embedded proprietary algorithms that lack transparent performance data. A test method was developed to assess the ability of a DCV controller to maintain the indoor CO2 concentration at a setpoint in response to a series of CO2 generation functions that represent three different building occupancy densities and two occupancy schedules. Six commercially available controllers were tested to demonstrate the method and provide directly comparable results. The performance (in terms of CO2 control and damper movement) of each controller tested was compared to the performance of an ideal controller which knows the CO2 generation function. Finally, the performance of a proportional-integral (PI) controller with preset gains was developed and tested to determine the potential maximum performance achievable with this control strategy. The best performing commercially available controller achieved CO2 control (within 75 ppm of the setpoint) approximately 80 % of the time with damper movement slightly less than an ideal controller. However, most of the commercially available controllers had marginal or poor performance for CO2 control and damper movement. Two controllers had damper movement more than three times the ideal controller. Notably, a PI algorithm configured and tested by the research team achieved superior performance with CO2 control 92 % of the time and damper movement 1.5 times the ideal controller.

Detailed assessment of hybrid ventilation control system in a mixed-mode building in cold climate

This paper investigates a hybrid ventilation control method for a mixed-mode office landscape in a cold climate, i.e. Norway. The mixed-mode building utilizes a hybrid ventilation system that combines natural ventilation through automatic window opening with a balanced mechanical ventilation system controlled by demand control ventilation (DCV) method. For natural ventilation, the windows consist of two parts: the upper part which is openable and the lower part that is not openable and equipped with external solar shading. From a control point of view, the article elaborates a control algorithm for the proposed hybrid ventilation based on various parameters including outdoor air temperature, indoor air temperature, CO2 level, maximum air change per hour (ACH), operation schedules, and heating/cooling setpoints. The simulation results obtained from the hybrid ventilation and mechanical ventilation cases are compared based on thermal comfort, CO2 level, and energy savings. The primary results show that the proposed hybrid ventilation control method can satisfy the thermal comfort and CO2 level requirements while reducing the energy use for fan and ventilation cooling by approximately 55% and 45%, respectively. While the impact on energy consumption for space and ventilation heating is negligible.

Towards healthy and energy-efficient buildings in the context of Egypt: Modelling demand-controlled ventilation to improve the indoor air quality in a generic office space in Cairo

Cairo is characterised by high concentrations of ambient air pollutants, especially air particulate matter of diameter less than 2.5 micrometre (PM2.5). Many studies have emphasized the impact of PM2.5 on people’s health and wellbeing including a World Bank report that has attributed 12% of the total annual deaths in Cairo to the exposure to ambient PM2.5. On one hand. improving the energy efficiency of buildings may involve implementing energy efficiency measures that aim to achieve indoor thermal comfort by maximizing the use of natural ventilation and minimizing mechanical air-conditioning. However, while natural ventilation can help reduce CO2 levels, it can also potentially lead to an increase in indoor PM2.5 levels. This study aims to investigate the impact of multiple air filtration scenarios on the energy consumption and the indoor air quality for a shoebox model that aims to represent generic offices Cairo. The study uses EnergyPlus simulations that leverage an Energy Management System script to model the demand-controlled ventilation, apply air filters when required, and simulate the increase in energy use due to the relevant pressure drops in the air system. The results for the scenarios investigated in the study highlighted that air filters can reduce the average indoor PM2.5 levels by nearly 40% during occupancy hours while causing an estimated increase of around 2-7% in the total operational energy. Given data and assumptions relevant to the study context, it was found that filtering the recirculated air while minimizing the introduction outdoor fresh air can be sufficient to minimize indoor PM2.5 levels.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

Indoor environmental quality in schools: NOTECH solution vs. standard solution

Background: In many Danish schools, the indoor environmental quality (IEQ) is challenging and studies document a poor IEQ in a majority of existing schools. Municipalities cannot afford comprehensive renovations and expensive mechanical ventilation solutions, hence public schools often suffer from poor indoor environment conditions. This study tests a new façade based, demand-controlled ventilation solution called NOTECH in the renovation of school. The study tests NOTECH vs. existing mechanical ventilation solution, comparing performance of both solutions at Skovbrynet Skole in Denmark. Methods: The project implements and investigates the effect of the NOTECH solution in a primary school classroom, comparing it to a similar classroom with conventional, mechanical ventilation. Methodically, indoor environmental quality and energy performance is monitored in the two identical classrooms during one school year 2018 - 2019. Results: The results show that both systems keep the conditions within acceptable limits and CO2 levels below 1000 ppm, which is the requirement according to the Danish Building Regulations. In terms of costs, the NOTECH system has a lower overall cost than the mechanical ventilation system, with total estimated costs for installation, heating, electricity and maintenance amounting to approximately 35% of the mechanical system’s costs. Finally, the results show that the NOTECH solution has a smaller embedded CO2 footprint for building materials, reducing the estimated carbon load by 95% compared to the mechanical ventilation solution. Conclusions: The performance of the two systems roughly is the same in relation to the indoor environmental quality, temperature, CO2 levels and relative air-humidity. Costs for implementation, energy consumption for heating and CO2 footprint for building materials however, are significantly lower for the NOTECH solution, compared to the mechanical solution.

Minimum sensor grid density and configuration to enable CO2-based demand-controlled ventilation in an office building

CO2-based demand-controlled ventilation (DCV) requires placement of CO2 sensors in air handling units (AHUs) as well as in individual zones. In complex multi-zone systems or across building portfolios, the installation and maintenance costs of these sensors are non-negligible. This study explores how CO2 sensor grids can be configured in a sparser manner to allow for CO2-based DCV of spaces adjacent to zones with CO2 sensors that may not have sensing infrastructure themselves. A simulation-based study of a 26-zone office building was conducted under a variety of occupancy schedules, number and placement of zone-level CO2 sensors, and with and without motion detectors to determine how RP-1747 DCV impacted heating, ventilation, and air conditioning (HVAC) energy use and zone CO2 concentrations when implemented traditionally or with adjacent control, for a total of 29,730 simulations. It was found that CO2 sensors installed in approximately 31% of zones could effectively enable CO2-based DCV across the entire floor plate with a negligible impact on the number of hours where CO2 concentrations were elevated, resulting in 7.3% to 17.4% higher HVAC energy savings compared to individual control of spaces in the case study building.

Outdoor thermal condition based-segmented intermittent demand-controlled ventilation for constant-air-volume system

The outdoor air supply is mandatory for healthy built environments but consumes a large amount of building energy. The demand-controlled ventilation method adjusts the outdoor air supply for both indoor air quality and high energy efficiency. However, the conventional demand-controlled ventilation method does not apply to the constant-air-volume ventilation system. Since the constant-air-volume ventilation system is widely used in practice for outdoor air supply, this study proposes a novel demand-controlled ventilation method, i.e., the segmented intermittent demand-controlled ventilation, to improve the energy efficiency of the constant-air-volume ventilation system with indoor air quality. The proposed method intermittently supplies outdoor air to reduce the amount of outdoor air supply for energy saving of ventilation fan while satisfying indoor air quality. Moreover, the proposed method divides the working time into segments and adjusts the outdoor air supply of each segment according to the outdoor weather for cooling/heating energy saving. This study reveals the detailed mechanisms of energy saving of the proposed method and develops an optimization algorithm to maximize the energy saving of the proposed method with indoor air quality. The proposed method is tested for both cooling and heating scenarios in different climate zones. The results show that compared with the conventional methods and the existing intermittent demand-controlled ventilation, the proposed method achieves weekly energy savings of 18.0%–62.4% and 4.3%–6.8% respectively. The proposed method can contribute to the development of low-carbon and healthy built environments.

Unlocking ventilation flexibility of large airport terminals through an optimal CO2-based demand-controlled ventilation strategy

Large airport terminals consume significant energy due to their extensive ventilation demands. Optimizing the operation of ventilation systems is a crucial aspect of achieving greater energy flexibility and efficiency. In this study, we proposed an optimal CO2-based demand-controlled ventilation (DCV) strategy that utilizes the large indoor space of airport terminals to shift ventilation loads, reduce operating cost, and enable demand response (DR) programs. The effect of air infiltration on ventilation demand is considered in the DCV strategy through on-site CO2 measurement. A passenger dwell model is integrated to estimate the occupancy of airport terminal based on flight schedules for dynamic ventilation control. Considering the DR signal and time-of-use tariff, an advanced evolutionary algorithm is employed to locate optimal ventilation solutions that deliver better economic performance and energy flexibility while meeting indoor air quality constraints. Using an airport terminal in Beijing, China, as a testbed, the superior performance of the proposed DCV strategy was demonstrated against a baseline strategy with a fixed outdoor air supply rate under three scenarios. Simulation results showed that the proposed DCV strategy can reduce peak energy consumption by up to 60.9% and operating costs by up to 48.6% in different scenarios. Moreover, the DCV strategy can provide a flexible trade-off between the overall energy efficiency and DR capability in the adjustable DR scenario. This study revealed great opportunities for improving the flexibility of ventilation in large-space buildings and provided an instructive case study.

Machine learning applications for a better demand controlled ventilation system experience in buildings: a review

PurposeAt the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).Design/methodology/approachThe method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.FindingsThe discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO2) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.Originality/valueFinally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO2 based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

Demand control and constant flow ventilation compared in an exhaust ventilated bedroom in a cold-climate single-family house

ABSTRACT A convertible, zoned ventilation system was field-tested in a modern, airtight Swedish home when occupied either by an experimental team or by a family. Indoor air quality in the master bedroom was monitored under four ventilation strategies. Relative to constant air volume strategies (CAV), demand-controlled ventilation (DCV) that was responding to CO2 concentration extracted more air when people were present, but less in total over 24 h. This elevated the indoor air humidity, beneficial in climates with dry winter air. Multiple monitors within the bedroom indicated that vertical CO2 stratification occurred routinely, presumably due to low mixing of supply air from a wall-mounted diffuse vent, spreading the air radially over the wall. This seemingly improved air quality in the breathing zone under local (ceiling) extract ventilation but worsened it during more typical, centralised extract ventilation, where air escapes the room via an inner doorway. The local extract arrangement thus seemed to yield both improved ventilation efficiency and reduced contaminant spread to other rooms. The noted air quality variations within the room highlight the importance of sensor placement in demand-control ventilated spaces, even in small rooms such as bedrooms.

Optimizing demand-controlled ventilation with thermal comfort and CO2 concentrations using long short-term memory and genetic algorithm

Heating, ventilation, and air conditioning systems are the largest consumers of energy in commercial and residential sectors. Minimizing their energy consumption without compromising indoor air quality and thermal comfort would result in environmental and financial benefits. However, most buildings still utilize constant air volume systems with on/off control to meet the thermal loads. Previous studies show that CO2-based demand-controlled ventilation is the most widely used strategy to determine the optimal level of supply air volume. Conventional CO2 mass balanced models, however, do not take the thermal comfort level into account. This paper aims to develop and implement a practical and efficient optimization framework to help office building designers and operators enhance thermal comfort and indoor air quality while minimizing energy costs for ventilation using demand-controlled ventilation. The objective function was constructed to consider thermal comfort indices, indoor air quality, and ventilation power demand. Long Short-Term Memory networks in conjunction with a generic algorithm optimization were utilized to generate the search spaces and solve the objective function. The proposed optimization strategy achieved near-optimal results within approximately 1 h of computation time. The findings indicate a reduction of the average ventilation power by 5.6% compared to the current on/off control approach and a slight increase of 0.25% in ventilation power when compared against the minimum ventilation rate recommended by ASHRAE. Additionally, the optimized approach leads to a saving of 26.9 kg per day of greenhouse gas emissions in terms of carbon dioxide equivalent.

A novel quantitative assessment framework of the IAQ resilience performance of buildings: The resilience score metric

Resilience performance in building design has emerged as a critical consideration in the face of increasing uncertainties or ‘shocks’ posed by natural disasters, climate change & excessive pollution. To evaluate and optimize building design decisions, holistic performance metrics are needed. This work defines a novel quantitative assessment framework of indoor air quality (IAQ) resilience which output is the resilience score (RS) metric that integrates all resilience aspects and building-relevant pollutants. The framework was demonstrated via simulations on a validated model of a case study educational building in Belgium for 3 ventilations systems: constant air volume (CAV), demand-controlled ventilation (DCV), DCV without filters, and 3 shock types (mechanical MS, internal IS, outdoor shocks OS). Results showed that OS was the least critical shock type, followed by IS and MS. With increasing degree of shock, RSMS decreased for all 3 systems linearly by 64% until 2.8 h of power outage, beyond which the rate of decrease slowed down considerably. CAV & DCV had the same RSMS and that of DCV w/o filters was 13% lower. The RSIS for CAV & DCV deteriorated by 38.8% & 46% before plateauing at values of 0.45 & 0.36 respectively. CAV had better resilience against IS. The effect of filters did not reflect in the RSIS. The RSOS for CAV & DCV deteriorated by 51% & 26% before plateauing at values of 0.49 & 0.73 respectively. DCV had the best resilience against OS, followed by DCV w/o filters & CAV.