Globally, buildings account for approximately 40% of annual energy consumption, driven by population growth, an increase in housing, and rising standards of living. This energy is predominantly used for lighting, heating, cooling, and air conditioning (AC), with global energy demand rising by 2.3% in 2018, the highest increase in the past decade (International Energy Agency, 2019). The extensive energy use in buildings, coupled with its quality, places immense pressure on the environment, leading to significant carbon emissions from the construction sector. This contributes to increased pollution levels indoors and outdoors, threatening health, energy security, and exacerbating climate change. Countries in arid climates face heightened challenges, such as elevated cooling demands and a higher incidence of heat-related health risks like heat stress (Harlan et al., 2006). For instance, the United Arab Emirates (UAE) consumes significantly more energy than other nations in the region (IRENA, Renewable Energy Market Analysis, 2019). Similar to global trends, rapid urbanization, population growth, and energy-intensive industries drive the UAE’s high per capita energy consumption. Its hot, arid climate necessitates frequent use of cooling systems, which dominate energy consumption in buildings (IRENA, Renewable Energy Market Analysis, 2019). AC units account for 60%–65% of a building’s electricity use, contributing significantly to carbon emissions and the release of heat-trapping gases (Mardiana and Riffat, 2015; UN Environment and International Energy Agency, 2017; Elnabawi, 2021). Rising annual temperatures further increase dependence on mechanical cooling, thereby intensifying climate change.

To mitigate these impacts, there has been a shift toward passive energy conservation strategies, such as natural and hybrid ventilation systems, as alternatives to traditional AC (Pfafferott et al., 2004; Pollock et al., 2009; Bianco et al., 2009; Elnabawi and Saber, 2021; Abdulla Kutty et al., 2024). These systems aim to enhance indoor comfort while minimizing energy consumption and environmental impact. Natural ventilation is a widely used method for regulating indoor climates and achieving air quality standards without excessive energy use (Pfafferott et al., 2004; Annan et al., 2014). Research shows natural ventilation is effective for about 52% of the year in the Middle East (Annan et al., 2014). Another study in the UAE reported energy savings of 10%–40% when natural ventilation was utilized during moderate temperatures (Taleb, 2015). However, natural ventilation alone is insufficient for maintaining year-round thermal comfort, particularly in extreme climates where productivity can be compromised (Fiorentini et al., 2019; Ledo Gomis et al., 2021). Its efficiency also depends on local climatic conditions, making it unsuitable for certain regions (Gomis et al., 2020). Hybrid ventilation systems address these limitations by prioritizing natural ventilation during mild conditions and switching to mechanical systems during extreme heat (Griffiths and Eftekhari, 2008; Bianco et al., 2009; Ezzeldin and Rees, 2013; Elnabawi and Saber, 2021). This approach ensures indoor comfort while significantly reducing the energy consumption associated with mechanical cooling.

The hybrid ventilation approach optimizes energy efficiency and occupant comfort by adapting ventilation modes to seasonal and ambient conditions (Alves et al., 2015). While natural ventilation is employed for extended periods, the hybrid mode maintains acceptable comfort levels (Humphreys M. A. and Nicol J. F., 1998; Alves et al., 2015; Wang and Greenberg, 2015). Despite its potential, most research on hybrid systems has focused on temperate climates in North America and Europe, with limited application to the Middle East (Ezzeldin and Rees, 2013; Daaboul et al., 2018; Gomis et al., 2020; Elnabawi and Saber, 2022). For example, a review found only four studies on hybrid ventilation in the Middle East over the past decade (Gomis et al., 2020), while another analysis of 174 publications found just nine focused on hot, arid climates (Peng et al., 2018). Soebiyan (2021) reported that hybrid systems save 50% of energy in hot, arid climates, compared to 60%–70% in temperate regions and 28% in warm, humid areas. The limited research on hybrid ventilation in hot climates reflects challenges, such as regulating natural ventilation under high-temperature conditions. Effective cooling in such climates requires external air conditions to surpass indoor temperature and humidity levels, necessitating careful consideration of wind speed, direction, and air quality.

Ensuring thermal comfort in hybrid systems presents unique challenges. While factors such as indoor air quality, visual comfort, and acoustics are important, thermal comfort is often considered the most critical (Frontczak and Wargocki, 2011). Establishing universal comfort standards for hybrid buildings is difficult, as occupants of naturally ventilated buildings tend to tolerate broader temperature ranges than those in mechanically conditioned spaces. This tolerance varies significantly based on climate and location (Humphreys et al., 2015; Elnabawi and Hamza, 2020). Promoting natural ventilation over mechanical cooling could enhance occupants’ thermal adaptability (Carrilho da Graça and Linden, 2016). Adaptive comfort models, such as ASHRAE 55 and CIBSE TM52, are more suitable for evaluating naturally ventilated buildings compared to traditional PMV models. Further research is needed to explore adaptive comfort, especially in hot, arid climates, to accurately assess the benefits of natural over mechanical cooling. By directly exposing occupants to external air, natural ventilation fosters thermal equilibrium, which is often unattainable with mechanical systems.

Smart ventilation systems, leveraging advanced technologies like artificial intelligence (AI) and the Internet of Things (IoT), offer a promising solution to enhance energy efficiency and occupant comfort (Liu et al., 2020; Goli et al., 2020; Elnabawi and Hamza, 2024; Gholami et al., 2022). These systems dynamically adjust ventilation rates using real-time data from sensors, weather forecasts, and occupant preferences, optimizing energy use and indoor air quality (Elnabawi and Hamza, 2024; Gholami et al., 2022). AI-powered algorithms predict future ventilation needs and proactively adjust systems, while IoT-enabled sensors facilitate data-driven decision-making and remote monitoring. Integrating smart ventilation with HVAC and lighting systems enables holistic optimization of the built environment, resulting in significant energy savings and improved occupant wellbeing. Despite these advancements, research on the long-term performance and scalability of smart ventilation systems across various climates and building types remains limited, underscoring the need for comprehensive field studies and standardized testing procedures. Existing literature often emphasizes theoretical models over empirical data, limiting the understanding of real-world performance and user interactions (Ghahramani et al., 2020; Petrie et al., 2018). Moreover, a disconnect exists between precise thermal comfort modeling in hybrid ventilation buildings and general studies on thermal comfort improvements through machine learning and IoT. This fragmented approach, as highlighted by Kariminia et al. (2016) and Jeong et al. (2022), hinders the development of holistic solutions. Further research is necessary to explore hybrid ventilation implementation in hot climates, establish consistent thermal comfort standards, and manage systems reliant on fluctuating external microclimates.

To address these challenges, this review advocates for a paradigm shift, integrating traditional approaches with Industry 4.0 technologies in order to develop a robust, region-specific hybrid/mixed ventilation design guide through “Knowledge Translation.” While Industry 4.0 encompasses a broad spectrum of advancements, this paper focuses on its potential to foster human-machine interaction. This entails seamless integration and collaboration between humans and intelligent machines, leveraging the unique capabilities of AI, IoT, and VR/AR for enhanced efficiency. Table 1 summarises key Industry 4.0 applications that can optimise hybrid/mixed ventilation implementation, addressing system management, thermal comfort, and energy efficiency. By assimilating information from digital twins, GIS, ML, VR, MR, and IoT, Industry 4.0 can pave the way for hybrid/mixed ventilation buildings with high prediction accuracy, ensuring both comfort and sustainability in challenging climates.

This study presents a comprehensive review of the effectiveness of hybrid ventilation systems in achieving thermal comfort located within hot, arid climates zones similar to the Middle East. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA), the review adopted a rigorous four-step methodology for a study selection, mirroring the approach outlined by Umar (2020), Liberati et al. (2009) and Carlucci et al. (2020): identification, screening, eligibility assessment, and final inclusion (as illustrated in Figure 1). This systematic process ensured the inclusion of only the most relevant research. The selection criteria prioritised studies that: investigated mixed-mode ventilation systems (specifically combining natural and mechanical ventilation) in buildings; focused on arid climate regions; evaluated the impact of hybrid ventilation on occupant thermal comfort and the effectiveness of different control strategies; which have been published in English with the full text readily accessible. This meticulous approach, encompassing a review of over 80 research articles published between 2010 and the first quarter of 2024, ensured the inclusion of high-quality, pertinent data, ultimately strengthening the reliability and validity of the review’s findings.

Underpinning this research is a post-positivist research philosophy, evident in the emphasis on objectivity and systematic analysis. The use of established guidelines lsuch as PRISMA, coupled with the rigorous four-step methodology, reflects a commitment to minimising bias and maximising the replicability of the research process. This approach aligns with the post-positivist aim of uncovering objective knowledge by rigorously examining existing research and synthesising findings in a structured, transparent manner.

Three primary techniques are employed to estimate energy and thermal performance: mathematical modelling, experimental analysis, and computer simulation (Zhai, 2003; García-Fuente et al., 2022). While mathematical modelling can be complex and highly sensitive to minor variations, experimental approaches have been proven to be impractical for evaluating multiple design scenarios due to their time-consuming and costly nature. Computer simulation, however, mitigates these limitations (Bano and Sehgal, 2018), offering greater feasibility, accuracy, and informativeness (Zhai, 2003). Recognising the advancements in computer simulation methodologies after 2010 (Elnabawi and Raveendran, 2024), this review focuses on studies published from 2010 onwards. To ensure a comprehensive and unbiased literature review, a rigorous and multi-step screening process, illustrated in Figure 1, was implemented to ensure the selection of high-quality, relevant studies for this literature review. Initially, 84 articles were identified through a comprehensive search of five prominent academic databases using a defined set of keywords and search parameters, as detailed in Table 2. This initial pool of articles underwent a preliminary screening based on titles and abstracts to identify studies potentially meeting the inclusion criteria (Umar, 2021). Following this initial screening, the full texts of the remaining articles were retrieved and assessed against the full set of inclusion criteria. Only peer-reviewed journal articles published in English that met all inclusion criteria–focusing on the performance of hybrid ventilation systems in buildings within hot climates, utilizing empirical measurements or validated simulations–were included in the final analysis. This rigorous process resulted in the selection of 21 articles deemed relevant and suitable for inclusion.

This section analyses trends in research on mixed-mode building performance in hot climates, similar to the Middle East from 2010 to the first quarter of 2024. It summarises key aspects of these studies, including publication year, building typology, comfort standards employed, and assessment methods.

Despite the prevalence of hot-dry climates in regions like the Middle East, research on hybrid ventilation systems for reducing cooling energy consumption remains limited. Most existing studies focus on climates in North America and Europe, leaving a significant research gap in addressing the specific needs of hot climates. Table 3 summarizes various studies investigating the potential of hybrid ventilation systems to enhance cooling energy efficiency in buildings located in hot regions. These studies explored diverse operational strategies for hybrid ventilation, reflecting a range of approaches to system optimization.

Some studies, such as those by Ezzeldin and Rees (2013), Rizk et al. (2015), and Rashad et al. (2024), focused on switching to natural ventilation when outdoor conditions were favorable. Others, like Brittle et al. (2016) and Daaboul et al. (2018), adopted fixed schedules aligned with typical office hours, emphasizing operational simplicity. Taleb (2015), meanwhile, explored various HVAC operation scenarios during the winter months. Additionally, researchers examined different hybrid ventilation modes. While Ezzeldin and Rees (2013) and Brittle et al. (2016) concentrated on the Change-Over mode, Abou Hweij et al. (2017) investigated the Concurrent mode. Other studies, including those by Chenari et al. (2016), Ledo Gomis et al. (2021), and Muhy Al-Din et al. (2023), explored multiple operational modes. However, a comprehensive analysis comparing the performance of these modes under various hot-dry climate conditions is still absent, highlighting a critical gap in the literature.

Although frameworks like Leadership in Energy and Environmental Design Accredited Professional (LEED AP) provide valuable guidelines for hybrid ventilation in hot and humid climates (Merabet et al., 2021), they often lack detailed performance metrics and empirical data tailored to the unique challenges of hot-dry environments. This underscores the urgent need for more targeted research to develop comprehensive, context-specific design guidance, enabling the optimization of hybrid ventilation systems for energy efficiency and occupant comfort in these climates.

This section highlights key trends observed in hybrid ventilation research, including the challenges and opportunities by drawing upon data from the reviewed studies.

• The recent surge in research on hybrid system controls for mixed-mode buildings in hot climates reflects a growing recognition of their importance in a world facing increasing global temperatures and a greater emphasis on sustainable building practices. This upward trend in publications highlights the dynamism of this evolving field, driven by technological advancements in building systems and the potential for future innovation. The increasing interest in research further underscores the perception of hybrid ventilation as an effective measure for reducing energy consumption in buildings.

This analysis of hybrid ventilation design reveals a crucial need to move beyond standardised approaches and embrace context-specific solutions. Over-reliance on a single comfort standard, coupled with the wide variation in cooling setpoints, highlights the importance of considering local climates, building characteristics, and occupant preferences. Furthermore, our review has identified a significant gap in current research: most reviewed articles lacked a climate-specific focus, with a majority of studies originating from North America and temperate climates. This geographic bias limits the applicability of findings to diverse climate regions, particularly hot, arid climates where adaptive comfort considerations are paramount. Addressing these nuanced factors and achieving truly adaptive, occupant-centric hybrid ventilation systems necessitates a paradigm shift in design and operation. The integration of Industry 4.0 technologies offers a promising pathway to address these challenges and unlock the full potential of hybrid ventilation for a more sustainable, comfortable built environment.

This comprehensive review of recent research on hybrid ventilation systems for cooling energy reduction reveals several key trends as well as highlights critical areas for advancement. And while hybrid ventilation shows promise for energy-efficient buildings, realizing its full potential necessitates leveraging AI and Industry 4.0 technologies to address key research gaps (Alavi et al., 2021). This transition from traditional ventilation methods to AI-powered systems can be facilitated through the application of dynamic programming. This approach allows for subtle, yet impactful, changes in the system’s performance. By leveraging dynamic programming, the ventilation system can continuously adapt and optimize its operation based on real-time data, weather forecasts, and occupant preferences. This dynamic adaptation enables the system to respond to changing conditions, leading to enhanced energy efficiency and improved occupant comfort. The integration of dynamic programming with AI-driven control algorithms can create a more responsive and intelligent ventilation system, capable of anticipating future ventilation needs and proactively adjusting the system’s operation. This subtle, yet significant, shift towards a more dynamic and adaptive approach can pave the way for a more sustainable and comfortable built environment. This approach is exemplified in Figure 6, which proposes a framework based on the seamless integration of four application levels: hybrid system controls, heating and cooling setpoints, thermal comfort standards, and minimum and maximum cooling reduction. By identifying the shortcomings of conventional methods at each level and proposing Industry 4.0-based solutions, the framework not only transcends the limitations of existing research but also offers practical guidelines. Additionally, it suggests specific tools and instruments for each phase of implementation.

For instance, Traditional approaches to hybrid ventilation system controls often rely on simpler methods like the “Change-Over” mode, where the system switches between natural and mechanical ventilation based on predefined conditions or schedules. This approach is favoured for its ease of implementation. While these systems may incorporate real-time monitoring to leverage favourable outdoor conditions for natural ventilation, they often operate on fixed schedules, typically aligned with standard office hours. However, these traditional approaches have limitations. They lack the dynamism to adapt to rapidly changing conditions or individual occupant preferences, limiting their ability to optimise energy efficiency and indoor comfort. Furthermore, the opportunistic use of natural ventilation, while promising, necessitates robust and reliable monitoring and control systems.

This is where Industry 4.0 technologies offer significant advantages. By integrating geo-spatial digital twins and the Internet of Things, there is the potential to create smarter hybrid ventilation systems. IoT sensors distributed throughout the building can measure various environmental parameters, including temperature, humidity, wind speed, and solar radiation. This real-time data, collected and analysed through the IoT network, allows for a more accurate and dynamic understanding of the thermal conditions pertaining to the buildings.

Furthermore, while hybrid ventilation systems show great potential for energy efficiency, the traditional methods for defining and achieving thermal comfort within these systems have significant limitations. Studies reveal a wide range of cooling setpoints (23°C–29°C) used in hybrid ventilation, highlighting the inadequacy of a generic approach. Thermal comfort is not solely determined by temperature but is influenced by a complex interplay of factors, including climate, building type, and individual preferences. Over-reliance on standardised thermal comfort models like ASHRAE Standard 55, while prevalent in research, further underscores this limitation. Research suggests that occupants of naturally ventilated buildings, particularly in certain climates, may tolerate a wider temperature range than these standards typically account for. This is where Industry 4.0 technologies offer a path towards more nuanced and personalised comfort solutions. By integrating wearable sensors, mobile applications, and advanced simulation environments, we can gather both physiological data and subjective feedback from occupants. This data, combined with real-time environmental information, can be used to develop personalised comfort models and adaptive control strategies. Imagine a system that understands your individual preferences and dynamically adjusts the indoor environment to ensure your comfort, regardless of external conditions. Furthermore, simulation and virtual reality technologies allow us to test and optimise different design strategies for thermal comfort before physical implementation, accelerating the development of more effective and responsive hybrid ventilation systems.

The true power of Industry 4.0 lies in its ability to process this data and translate it into actionable insights. Intelligent algorithms and predictive models can be developed to anticipate favourable outdoor conditions and preemptively adjust ventilation modes. This proactive approach ensures optimal energy performance without compromising occupant comfort. In essence, Industry 4.0 paves the way for hybrid ventilation systems that are not only more efficient but also more responsive to the dynamic needs of both the building and its occupants.

While Industry 4.0 technologies hold immense potential for creating more comfortable and personalised hybrid ventilation systems, their effectiveness hinges on a robust foundation of research and data. This is where AI can play a crucial role, by addressing a key challenge: the lack of standardised research and reporting in the field.

Currently, the ability to compare findings across different studies and draw meaningful conclusions is hampered by inconsistencies in data collection, analysis, and reporting. AI-powered platforms offer a solution by enabling the analysis of vast datasets from diverse sources, including building management systems, weather stations, and even research papers (Luo et al., 2020; Pang et al., 2021). This data-driven approach can facilitate the development of standardised performance metrics and benchmarks, enabling more accurate comparisons across studies and facilitating meta-analyses (Gao and Malkawi, 2014; Zhang et al., 2018). Imagine a future where researchers can easily access and analyse a global repository of hybrid ventilation data, all standardise and readily comparable. This would significantly accelerate the pace of innovation and knowledge sharing in the field.

Furthermore, AI algorithms offer a powerful tool for advancing hybrid ventilation research by automating the tedious process of extracting relevant information from research papers and technical reports, as highlighted by Chong et al. (2017). This streamlined knowledge synthesis and dissemination allows researchers to focus on higher-level analysis and interpretation, ultimately leading to a more comprehensive understanding of hybrid ventilation systems and their potential. Additionally, existing building performance models can provide invaluable data for optimizing the design and retrofitting of hybrid ventilation systems, particularly in challenging hot climates. These models can simulate the complex thermal and moisture dynamics within a building, accounting for factors like occupancy patterns, environmental conditions, and energy consumption. By leveraging this data, designers can gain a clearer understanding of how different hybrid ventilation strategies will perform under various scenarios. This allows for the optimization of system design and controls to enhance energy efficiency, indoor air quality, and occupant comfort. Moreover, insights from these model-based analyses can inform the integration of predictive control algorithms, enabling hybrid ventilation systems to adapt to real-time changes in occupancy and environmental conditions, further improving their effectiveness. By incorporating data from existing building performance models, researchers and practitioners can develop more robust and versatile hybrid ventilation solutions that address the unique challenges of hot climates, paving the way for more sustainable and user-centric building environments.

This standardised, data-rich environment fostered by AI not only benefits researchers, but also paves the way for addressing another crucial aspect of hybrid ventilation: occupant behaviour and engagement. After all, even the most sophisticated system will fall short if it doesn't align with the needs and preferences of its users.

Here again, AI offers powerful tools: Machine learning algorithms can analyse occupant preferences, patterns, and interactions with hybrid ventilation systems, enabling the development of personalised control strategies that cater to individual needs. Imagine a system that learns your preferred temperature range, anticipates your schedule, and adjusts the ventilation accordingly, ensuring your comfort throughout the day. Natural language processing and chatbots can also facilitate more intuitive user interfaces, empowering occupants to understand and interact with the system effectively. This increased transparency and control can lead to higher user satisfaction and greater acceptance of hybrid ventilation systems.

Beyond improving existing systems, AI is also crucial for expanding the applicability of hybrid ventilation to understudied building types and emerging technologies. By analysing large datasets, AI can identify trends and patterns, guiding research efforts towards areas with the highest potential impact (Goyal et al., 2018; Dong et al., 2019). For instance, machine learning algorithms can analyse energy consumption data from educational buildings to identify opportunities for optimising hybrid ventilation strategies during different school schedules and occupancy patterns (Gunay et al., 2016). Additionally, AI can accelerate the development and integration of emerging technologies, such as predictive control algorithms that anticipate future ventilation needs based on weather forecasts and occupancy patterns, further enhancing energy efficiency and occupant comfort (Zhan et al., 2021; Vadamalraj et al., 2020).

Hybrid/mixed ventilation research reveals a significant gap, despite the field’s potential for reducing cooling energy consumption, particularly in hot, arid climates. This gap stems from the complex, context-dependent nature of hybrid ventilation, leading to inconsistencies in reported performance and limitations in current approaches. The following conclusions can be inferred:

• Inconsistent Performance: While some studies show significant cooling load reductions (up to 65%) in hot climates, others report minimal or no energy savings. This inconsistency highlights the influence of various factors such as climate, building design, control strategies, and occupant behaviour, which are often overlooked in generalized approaches.

Addressing these challenges requires a shift towards:

• Integrating AI and Industry 4.0 Technologies: AI-powered platforms can analyse vast datasets to develop standardised performance metrics, enabling more accurate comparisons across studies and facilitating the development of context-specific design strategies. This data-driven approach can also optimise hybrid ventilation systems dynamically, ensuring optimal energy savings and thermal comfort for occupants.

To fully realize the potential of hybrid ventilation systems, future research should prioritize several key areas. First, developing standardized performance metrics and benchmarks is crucial to enable accurate comparisons across studies and facilitate informed decision-making. This standardization will be aided by integrating AI-powered platforms that can analyze building data, optimize system performance dynamically, and enable intelligent control strategies. Equally important is addressing the challenge of occupant engagement by developing personalized ventilation strategies that cater to individual preferences and behaviors. This requires further interdisciplinary research to understand the complex interplay between hybrid ventilation systems and varying occupant behaviors across different cultural and operational contexts. Finally, exploring the potential synergies between hybrid ventilation and other emerging technologies, such as predictive control algorithms and adaptive building envelopes, can unlock further efficiency and comfort gains. By addressing these research gaps and leveraging the power of AI and Industry 4.0 technologies, we can accelerate the transition towards wider and more effective implementation of hybrid ventilation systems, creating a more sustainable and comfortable built environment.