Impact of High-Efficiency Filter Pressure Drop on the Energy Performance of Residential Energy Recovery Ventilators

Abstract

As the importance of both indoor air quality (IAQ) and energy efficiency grows in residential buildings, the application of air filters in energy recovery ventilators has become essential. However, high-efficiency filters such as MERV 12 inevitably increase the pressure drop, adversely affecting the airflow, fan energy use, and heat exchange balance. This study quantitatively investigates how different levels of filter resistance—from clean conditions to 200% dust loading—affect system airflow, static pressure, exhaust air transfer, and power consumption. A standardized dust loading procedure was adopted to simulate long-term use conditions. The results show a 37% reduction in net supply airflow under heavily clogged filters, while the unit exhaust air transfer ratio increased from 7.2% to 17.7%, exceeding compliance limits. Surprisingly, electrical energy consumption decreased as the fan load dropped with the airflow. Despite an increase in the apparent heat exchange efficiency, this gain was driven by return air recirculation rather than true thermal effectiveness. These findings highlight the need for filter performance-based ERV certification and operational strategies that balance IAQ, energy use, and system compliance.

1. Introduction

The global imperative to mitigate climate change has fundamentally transformed building design paradigms, with zero-energy buildings emerging as a cornerstone of sustainable construction practices worldwide. In the United States, the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Standard 90.1-2022 has progressively tightened energy performance requirements, mandating energy recovery systems for buildings with specific outdoor airflow thresholds based on climate zones and operational hours [1]. These regulatory frameworks require building envelopes with enhanced airtightness and superior thermal insulation to minimize energy losses. However, these improvements simultaneously increase the importance of mechanical ventilation systems that can maintain acceptable indoor air quality (IAQ) while recovering energy from exhaust air streams. The paradigm shift is especially significant given the global deterioration in outdoor air quality, where concentrations of particulate matter (PM₁₀ and PM_2.5) continue to rise due to industrial activities, vehicular emissions, and transboundary pollution transport. Traditionally, ventilation systems were designed primarily to remove indoor-generated pollutants and maintain occupant comfort. However, contemporary ventilation design must address a dual challenge: removing indoor contaminants while simultaneously filtering and purifying incoming outdoor air, which may be more polluted than the indoor environment. This transformation is especially critical in regions experiencing persistent air quality issues, such as East Asia, where fine particulate matter concentrations frequently exceed World Health Organization guidelines [2].

The integration of high-efficiency filtration systems with energy recovery ventilators (ERVs) has become essential to meet both IAQ and energy efficiency objectives. International standards such as ISO 16890 have replaced earlier filter classification systems to provide more relevant performance metrics based on particulate matter capture efficiency for PM₁, PM_2.5, and PM₁₀ fractions [3]. In the United States, the Minimum Efficiency Reporting Value (MERV) system, outlined in ASHRAE Standard 52.2, remains the primary filter rating methodology, with ASHRAE recommending filters rated MERV 13 or higher for enhanced particle capture [4].

ERVs address this challenge by installing filters on both the supply and exhaust air streams to protect the heat recovery core from particulate contamination, ensuring that only purified air enters indoor spaces. However, implementing high-efficiency filtration introduces significant operational challenges. As filters accumulate particulate matter over time, the pressure drop across the filter media progressively increases, leading to higher fan power consumption and reduced heat recovery effectiveness. Previous studies have reported power consumption increases of up to 30.1% when HEPA filters are applied to residential ERVs, with the magnitude of the increase varying according to the ventilation flow rates and filter loading conditions [5].

Historically, product certification for ERVs in Korea was based on energy-related performance metrics, including the air volume, electric and sensible heat exchange efficiency, energy coefficient, and power consumption. However, with the recent implementation of government regulations targeting fine particulate matter, ERV energy performance is now required to be evaluated with the air filter installed to obtain certification. Nevertheless, only the use of certified filters is currently mandated, while quantitative assessments of their effects on ERV energy efficiency remain limited. Thus, there is a need for further research to evaluate how filter performance influences the overall system efficiency. The most recent revision of the KS B 6879 standard [6] includes expanded testing procedures such as leakage rate evaluations and filter life notification features. Despite these updates, quantitative studies on filter lifespan and its effect on ERV performance are still lacking, underscoring the urgent need for empirical data to inform future revisions of performance standards and testing methodologies.

Therefore, the objective of this study is to comprehensively analyze how the pressure drop induced by air filters affects the energy performance of ERV systems. The results are expected to provide foundational data for refining testing standards and to support the development of next-generation ventilation systems that are simultaneously high-performing and energy-efficient.

2. Literature Review

Numerous studies have demonstrated that the replacement cycle and efficiency of air filters significantly influence the energy consumption of HVAC. As filters accumulate airborne particles during operation, the resulting pressure drop increases the energy demand of HVAC fans, subsequently elevating the overall system energy usage. Thus, optimizing filter replacement not only reduces operational energy costs but also enhances system performance.

One of the key performance indicators in HVAC energy recovery systems is the effectiveness of the ERV. Factors such as the air velocity, mass flow rate, wheel rotation speed, and ambient air conditions significantly influence ERV performance. In foundational studies, Simonson and Besant [7,8] introduced dimensionless parameters and correlations to predict energy wheel effectiveness under varying outdoor air conditions, laying the groundwork for lifecycle cost evaluation models.

While high-efficiency filters such as HEPA types are typically assumed to increase energy consumption due to higher pressure drops, their actual impact can vary depending on the system configuration and operating conditions. For example, Cho et al. (2021) [5] reported that applying HEPA filters in residential heat recovery ventilators (HRVs) led to an increase in power consumption ranging from 13.5% to 41.3%, depending on the air change rate. Similarly, Yeap [9] demonstrated that fouled filters in systems utilizing permanent split capacitor (PSC) fans significantly increased energy demand due to excessive pressure drop. However, Stephens et al. [10] showed that the energy consumption difference between high- and low-efficiency filters could be negligible when appropriate fan control strategies are applied, and in some cases, the energy consumption per cooling ton even decreased. Filter replacement strategies have also been explored for energy and material efficiency. Ödling and Ullström [11] proposed a method to extend filter service life by monitoring the real-time pressure drop, thereby minimizing unnecessary replacements without compromising performance. Morgan et al. [12] evaluated an aspiration efficiency reducer (AER) in a full-scale HVAC environment, reporting up to 14% energy savings, a 34% reduction in particle infiltration, and a 75% extension of filter service life. Additional variables such as filter geometry, fan type, duct configuration, and system operating mode have also been shown to significantly affect the relationship between energy consumption and performance. Al-Azba and Mahgoub [13] proposed a parametric and multi-objective optimization framework for HVAC filter design. Their study demonstrated that appropriate filter selection can effectively balance IAQ and energy efficiency, especially under high ventilation load conditions. Further supporting this view, Stephens et al. [14] and Yeap [9] emphasized that systems with optimized filters and regular maintenance experience minimal energy penalties even when using high-efficiency filters.

3. Methodology

This study assessed the thermal exchange performance of an air-to-air energy recovery ventilator in accordance with the Korean Standard KS B 6879:2021 [6]. This standard outlines the testing apparatus, environmental conditions, measurement parameters, and performance evaluation criteria for ERVs. A ceiling-mounted, duct-type ERV unit with a nominal airflow capacity of 150 m³/h was selected for evaluation. This model is representative of units commonly installed in apartment complexes and public housing facilities in Korea. The unit was tested under two configurations: without a filter (baseline) and with a high-efficiency filter supplied by the manufacturer. The tests were conducted under rated airflow conditions. All tests were conducted using a certified multi-environment ERV performance test rig compliant with national standards. Figure 1 presents a schematic diagram of the experimental ERV system setup. The specifications of the experimental instruments are summarized in

Table 1. Summary of instrumentation.

Model	Purpose	Specifications	Locations
CHINO (PT100 A Class) (CHINO Corporation, Itabashi-ku, Japan)	Dry-bulb temperature	Measurement range of −30 °C to +70 °C, accuracy of ±0.1 °C, resolution is 0.1 °C	Supply air Return air Outdoor air Exhaust air
	Wet-bulb temperature	Measurement range of −30 °C to +70 °C, accuracy of ±0.1 °C, resolution is 0.1 °C
YOKOGAWA MX100 (Yokogawa Electric Corporation, Musashino, Japan)	Data logger (data acquisition unit)	Voltage range of −60.00 mV to 60.00 mV, accuracy of ±0.05%, resolution is 0.01 mV
VAISAKA (GMT222)	CO2 sensing	CO2 range of 0 ppm to 10,000 ppm, accuracy of ±1.5%
YOKOGAWA (EJA110E)	Differential pressure transmitters	Pressure range of 0.5 kPa to 5 kPa, accuracy of ±0.055%	Supply/exhaust air side
YOKOGAWA WT-333	Power meter (energy consumption)	DC power measurement accuracy 0.1% of reading +0.2% of range	ERV system

. Thermometers and hygrometers use resistance thermometers to measure the inlet and outlet temperatures for environmental control and performance measurement with an accuracy of ±0.1 °C. Electrical meters use indicating and totalizing types to measure voltage, current, and power with an accuracy of ±0.5%. Flow meters were used to measure airflow at the inlet and outlet with an accuracy of ±1.0%. All sensors used were calibrated.

To analyze the impact of the air filter pressure drop on the system performance of the ERV, we used the lifetime acceleration method by loading the actual filter with particulate matter. The filter loading simulation apparatus and dust injection protocol employed in this study were based on the experimental methodology proposed by [15], which standardized the procedure for evaluating ventilation filter performance under controlled dust exposure conditions using A2 Fine Test Dust (Power technology Inc., Alexander, AR, USA).

Figure 2 showed the dust loading test setup. The differential pressure (ΔP) across the filter was continuously monitored using a high-accuracy pressure transducer. After exposure, the test filter was weighed using a precision balance to determine the mass loading. Filters were categorized by pressure drop stages based on the initial and target ΔP thresholds. The differential pressure transmitter was calibrated within the 0.5–900 Pa range, sufficient to capture all experimental readings. Each trial utilized a new ISO 16890-certified filter tested under controlled laboratory conditions (23 ± 2 °C, 45 ± 5% RH). A2 dust was used for loading until the desired pressure differential was reached, simulating end-of-life resistance states.

This method ensured a quantifiable and repeatable loading process, enabling analysis of the relationship between particulate accumulation, pressure drop, and the resulting impact on airflow and system energy consumption. This configuration enabled the controlled analysis of air delivery, energy consumption, and system pressure balance under five filter resistance scenarios: baseline (no filter), pre-filter only, and pre-filter + MERV 12 filters at 50%, 100%, 150%, and 200% simulated clogging. Prior to each test condition, filters were weighed using a calibrated digital balance with a resolution of 0.01 g. The mass differences between the clean and loaded filters were recorded to estimate the particulate mass loading, which correlates with the pressure drop and flow degradation observed in the system.

Table 2. Schematic of each case with filter conditions.

Cases	Conditions	Initial Weight (g)	After Loading Weight (g)	Filter Front–Back Pressure Difference
Case 1	No filter	-	-	-
Case 2	Pre-filter with MERV 12 (pressure drop 50%)	163.7 g	169.6 g (+5.9 g)	304 Pa (initial 202 Pa)
Case 3	Pre-filter with MERV 12 (pressure drop 100%)	163.5 g	170.8 g (+7.3 g)	369 Pa (initial 202 Pa)
Case 4	Pre-filter with MERV 12 (pressure drop 150%)	168.2 g	185.8 g (+17.6 g)	436 Pa (initial 202 Pa)
Case 5	Pre-filter with MERV 12 (pressure drop 200%)	165.1 g	185.7 g (+20.6 g)	566 Pa (initial 202 Pa)

shows each case. Despite initial mass differences among the new filters, dust loading was performed until each filter reached a predefined differential pressure. The resulting mass gain and pressure drop show consistent trends, validating the comparability across test cases.

Figure 3 shows the airflow paths within the ERV core and photographs of the experimental setup. Arrows indicate the directional flow of the return, supply, exhaust, and outdoor air. Filter mass was monitored using a digital scale to quantify dust accumulation over time. The layout ensures precise tracking of air routing and filter placement.

To assess the impact of dust accumulation on system performance, three identical filters were installed at the supply air inlet of the ERV unit (Figure 3). The airflow pattern inside the heat exchanger was cross-type, with a counterflow configuration ensuring heat exchange between the exhaust and incoming outdoor air. As shown in Figure 3b, filters were positioned to prevent bypass leakage, and airflow directionality was strictly maintained following manufacturer guidance. The ERV used in this study was equipped with dual BLDC fans and typically operates under a constant-airflow control algorithm. However, the control logic was manually overridden to maintain a fixed 24 V input, allowing natural system response under increasing filter resistance. As the airflow decreased due to higher resistance, fan torque demand also dropped, resulting in stable or slightly reduced energy consumption despite degraded performance (

Table 3. Measurement conditions under heating conditions.

-	Indoor Condition		Outdoor Condition
	Dry-Bulb, °C	Wet-Bulb, °C	Dry-Bulb, °C	Wet-Bulb, °C
Test Conditions	22 $\pm$ 0.3	13.9 $\pm$ 0.2 (40.0%)	2 $\pm$ 0.3	0.4 $\pm$ 0.2 (75.1%)

).

We have added citations to ISO 16494 [16], KS B 6879 [6], and the relevant ERV performance literature for Equations (1)–(6), or noted where equations were developed in this study. The unit exhaust air transfer ratio can be expressed as

$$UEATR={\displaystyle \frac{C_{SA}-C_{OA}}{C_{RA}-C_{OA}}}\times 100 \left[\%\right]$$

(1)

where UEATR is the unit exhaust air transfer ratio (5), C_SA is the tracer gas concentration at the supply air outlet, C_OA is the tracer gas concentration at the outdoor air inlet, and C_RA is the tracer gas concentration at the return air inlet.

The net supply airflow can be expressed as

$$Q_{SANet}={\displaystyle \frac{100-UEATR}{100}}\times Q_{SA}$$

(2)

where Q_SAnet is the net supply airflow (m³/s), Q_SA is the supply airflow (m³/s).

Assuming the same flow rate across an ERV at test conditions, the sensible effectiveness can be expressed as

$${\epsilon }_s={\displaystyle \frac{T_{OA}-T_{SA}}{T_{OA}-T_{RA}}}\times 100 \left[\%\right]$$

(3)

and the latent effectiveness as

$${\epsilon }_l={\displaystyle \frac{W_{OA}-W_{SA}}{W_{OA}-W_{RA}}}\times 100 \left[\%\right]$$

(4)

and the total effectiveness as

$${\epsilon }_T={\displaystyle \frac{h_{OA}-h_{SA}}{h_{OA}-h_{RA}}}\times 100 \left[\%\right]$$

(5)

where ε_s is the sensible effectiveness, ε_l is the latent effectiveness, ε_T is the total effectiveness, T is the temperature of the air, W is the humidity ratio of the air, and h is the enthalpy of the air. The subscript OA is used for the outdoor air, SA for the supply air, and RA for the return air.

The coefficient of energy of the ERV is described by the following Equation (6):

$$COE={\displaystyle \frac{Q_{SANet}\left(h_{OA}-h_{SA}\right)\times 1000+P_{vma}}{P_{in}}}$$

(6)

where COE is the coefficient of energy (-), h_OA is the enthalpy of the outdoor air (kJ/kg of dry air), h_SA is the enthalpy of the supply air (kJ/kg of dry air), P_vma is the power value of the moving air (J/s), and P_in is the input power to ERV (W).

4. Results and Discussion

To evaluate the impact of filtration and pressure drop on ventilation performance, the air transfer characteristics were compared across five configurations, including a baseline “no filter” condition and four levels of pre-filter + MERV 12 filter assemblies with varying pressure drop scenarios (50%, 100%, 150%, 200%).

Figure 4 shows the variation in supply and exhaust airflow under different filter resistance levels. The supply airflow shows a clear decreasing trend as the pressure drop across the filters increases. Without any filter, the supply airflow remains above 150 m³/h, but it drops to approximately 108 m³/h under the highest resistance condition (pre-filter + MERV 12 with 300% pressure drop). This represents a 28% reduction, placing the performance well below the ±10% standard limit. Even under moderate resistance, the supply flow falls below the rated value, ranging between 125 and 120 m³/h, signaling compromised system performance. In contrast, the exhaust airflow exhibits minimal variation across all configurations, remaining largely within the performance compliance band (135–150 m³/h). This implies that the exhaust side of the ERV system is more resilient to pressure changes introduced by filtration, likely due to lower filter resistance. These results indicate a systemic imbalance introduced by high-resistance filters. While the exhaust path maintains near-ideal performance, the supply path degrades significantly, potentially leading to under-ventilation and degraded indoor air quality.

Figure 5 presents a comparative analysis of the net supply airflow ratio and unit exhaust air transfer ratio under varying filter configurations and pressure drops. The performance threshold for the exhaust air transfer ratio is defined as ±10%, indicated by the shaded compliance band. In the “no filter” condition, the system delivers a high net supply airflow ratio of 92.8%, with a minimal exhaust air transfer ratio of 7.2%, indicating effective ventilation with low cross-leakage between air streams. However, as the filter resistance increases, the net supply airflow ratio steadily declines—dropping to 82.3% under the most restrictive condition (pre-filter + MERV 12, pressure drop 200%). Thus, unless corrective design measures are introduced (e.g., fan capacity enhancement, dual-motor balancing), the use of high-efficiency filters could render otherwise-certified systems non-compliant when operating under real-world conditions. As shown in

Table 4. Summary of ERV performance metrics under carrying filter resistance.

Configuration (Filter Front–Back Pressure Difference)	Net Supply Airflow (m3/h)	Electric Energy Consumption (W)	Supply Air Static Pressure (Pa)	Unit Exhaust Air Transfer Ratio (%)	Total Heat Exchange Efficiency (%)	COE (-)
No filter	142	71	113.9	7.2	71	14.39
Pre-filter with MERV 12 (Δ102 Pa)	111	68	110.2	10.5	77	13.91
Pre-filter with MERV 12 (Δ167 Pa)	108	68	110.1	13.0	78	13.67
Pre-filter with MERV 12 (Δ234 Pa)	104	67	109.3	13.3	80	13.46
Pre-filter with MERV 12 (Δ364 Pa)	89	66	108.4	17.7	84	13.08

, the unit exhaust air transfer ratio increased with the level of filtration-induced pressure drop. The baseline condition without a filter exhibited the lowest transfer ratio of 7.2%, while the configuration with the highest pressure drop (pre-filter + MERV 12, pressure drop 200%) recorded the highest value of 17.7%. This suggests that the imbalanced resistance between the supply and exhaust pathways due to filter loading can exacerbate airflow deviation, potentially resulting in air leakage or bypass phenomena. Additionally, the net supply airflow ratio, a critical indicator of actual ventilation effectiveness, decreased consistently as the filter resistance increased. The ratio dropped from 92.8% (no filter) to 82.3% under the highest filter resistance. Conversely, the unit exhaust air transfer ratio increases with higher filter resistance, rising from 7.2% to 17.7%. This indicates a growing portion of exhaust air recirculating into the supply path, which can result in pollutant re-entrainment and compromised indoor air quality. While all conditions remained within the net supply airflow margin typically accepted in ERV operation, the exhaust air transfer ratio exceeded the ±10% threshold at all filter configurations except the baseline. The non-compliance suggests that filter integration leads to internal air leakage or mixing.

Figure 6 presents the measured electric energy consumption of the HRV system under different filtration configurations, ranging from no filter to pre-filter + MERV 12 with increasing pressure drop conditions. Interestingly, while filter resistance clearly affected airflow performance, its impact on electric energy consumption was relatively modest. Power consumption decreased slightly from 71 W (no filter) to 66 W under the most restrictive filter configuration. This is likely due to a reduction in actual airflow delivered, resulting in a lower overall fan load despite higher resistance. Pressure differentials across both supply and exhaust ducts remained relatively stable, with only slight variations (<5 Pa) observed across all configurations. This indicates that the ERV unit maintained overall pressure balance within acceptable operational limits, despite filtration-induced changes in flow characteristics.

Figure 7 illustrates the static pressure differential (Pa) for both the supply and exhaust air paths under various filter conditions. The supply air static pressure differential decreased from 113.94 Pa (no filter) to 109.25 Pa under the highest filter resistance condition. This decreasing trend contrasts with typical expectations that pressure should rise with added resistance. However, as demonstrated in prior airflow measurements, the reduction in volumetric airflow through the supply side due to high resistance results in lower overall dynamic pressure, thereby slightly reducing static pressure measurements.

This reflects a key insight: high-resistance filters may not produce a noticeable rise in static pressure if the airflow volume is simultaneously decreasing. It is not the pressure loss that increases, but the air delivery that decreases, masking the true resistance impact.

The exhaust air static pressure differential remained relatively stable across all conditions, ranging narrowly between 116.18 Pa and 116.58 Pa. This suggests that the exhaust pathway is more robust to filter-induced changes—likely due to the absence of significant upstream filtration or more consistent airflow control. The decoupled behavior between the supply and exhaust static pressures suggests a growing pressure imbalance within the ERV system as the filtration resistance increases. Such an imbalance can lead to unintended air mixing, filter leakage bypass, or even control system instability in real-world operation.

Therefore, monitoring only the pressure differential without accounting for the actual airflow may underestimate the degradation in system performance due to filtration.

The performance of the ERV system under varying filtration conditions was evaluated in terms of four key indicators: net supply airflow, electric energy consumption, supply air static pressure, and exhaust air transfer ratio. A comparative summary is provided in Table 4. As shown, the net supply airflow decreased significantly with increasing filter resistance. When no filter was applied, the system maintained a flow rate of 142 m³/h, but this dropped to 89 m³/h under the highest resistance condition—a 37% reduction. This flow reduction has direct implications for ventilation effectiveness.

The electric energy consumption also showed a decreasing trend, from 71 W (no filter) to 66 W (ΔP 364 Pa). This counterintuitive result stems from the reduced airflow volume, which lowered the fan workload despite increased filter resistance. While this may appear energy-efficient, it actually indicates under-ventilation, thereby undermining IAQ objectives. Most critically, the exhaust air transfer ratio, representing the degree of unintended mixing or leakage between air streams, increased sharply from 7.2% to 17.7%. This value exceeds the ±10% performance tolerance typically cited in system standards, suggesting the potential for re-entrainment of exhaust air contaminants into the supply stream. This not only undermines IAQ, but also raises concerns about energy wastage.

The heat exchange efficiency increased despite a significant reduction in net supply airflow as the filter loading progressed. While the supply airflow decreased from 142 m³/h (no filter) to 89 m³/h at 180% filter clogging, the heat exchange efficiency improved from 71% to 84%. This counterintuitive trend is presumed to be influenced by the notable increase in the unit exhaust air transfer ratio, which rose from 7.2% to 17.7% over the same range. The elevated exhaust air transfer ratio implies that a larger portion of the return air was recirculated and mixed within the system, likely enhancing the preconditioning of incoming air. Such internal mixing with relatively warmer return air (22 °C) may have reduced the temperature gradient between supply and exhaust air streams across the heat exchanger, resulting in a more efficient thermal exchange process. However, this must be carefully weighed against the potential degradation of ventilation effectiveness and indoor air quality. This improvement is likely attributable to the elevated unit exhaust air transfer ratio, which implies a greater portion of the return air is internally recirculated and mixed with incoming outdoor air. This preconditioning effect reduces the temperature difference across the heat exchanger, enhancing thermal effectiveness despite reduced net supply airflow.

5. Conclusions

In this study, we investigated the performance degradation of an energy recovery ventilator (ERV) as the pre-filter resistance increased, using five test configurations including a no-filter baseline and pre-filters with MERV 12 ratings subjected to various loading levels (50–200%). Key performance indicators evaluated were net supply airflow, electric energy consumption, static pressure, and heat exchange efficiency. To provide a more comprehensive assessment of system performance, we introduced COE as a composite metric.

• The COE values progressively decreased from 14.39 (no filter) to 13.08 (200% loading), confirming that increased filter resistance leads to cumulative performance losses across both thermal and airflow aspects. Our results indicate that while electric energy consumption remained relatively stable, airflow and heat recovery capacity deteriorated significantly under heavier filter resistance. This underscores the importance of timely filter replacement and performance-based maintenance strategies.
• To improve current standard testing practices (e.g., KS B 6879, ISO 16494), it is recommended to incorporate a filter aging module that simulates dust loading impacts across the filter’s lifecycle. This may include multi-stage loading protocols and pressure drop thresholds, as specified in ISO 16890. While the direct integration of aging protocols into international testing standards may require broader inter-laboratory validation and industry consensus, this study provides foundational data that can support the future development of a standardized ERV filter degradation test procedure.
• A coordinated control strategy that combines smart differential pressure sensors with variable-frequency drive (VFD) fans is technically feasible using current ERV hardware. Such a feedback-based approach can dynamically maintain airflow balance and mitigate energy waste under rising resistance conditions.
• These findings provide a technical basis for advancing performance-based ERV standards, and support the implementation of adaptive filter replacement algorithms in building ventilation systems.
• Future work will focus on expanding the experimental dataset under various filter loading conditions to build a practical database, which could inform policy recommendations and standard revisions. A mass–energy coupling model will also be developed to isolate the effect of internal recirculation on sensible heat recovery.