Optimizing HVAC System Design in High-Occupancy Spaces Using Infrared Imaging, IoT Sensing, and Energy Simulation

1. Introduction

Heating, ventilation, and air conditioning (HVAC) systems are essential for maintaining indoor comfort, energy efficiency, and air quality in high occupancy environments such as casinos. In Northwest Indiana, facilities like Ameristar, Horseshoe, Blue Chip, Hard Rock, and Four Winds face unique HVAC challenges due to continuous 24 hours operation, variable occupancy levels, and frequent structural modifications [1]. These issues contribute to poor zoning, airflow inconsistencies, and elevated energy consumption, all of which negatively affect guest comfort and operational efficiency.

Interviews conducted with facility managers and HVAC technicians at these five casinos revealed that only 20 percent of the facilities utilize advanced HVAC technologies such as IoT enabled sensors, AI driven predictive maintenance, or infrared thermal imaging [2,3]. The remaining 80 percent rely on traditional systems, underscoring a regional gap in the adoption of intelligent and responsive HVAC solutions. This study explores the integration of modern sensor driven HVAC technologies and evaluates their potential to address these operational inefficiencies through controlled simulation.

While recent literature highlights the potential of IoT, AI, and thermal imaging in optimizing HVAC performance across residential, institutional, and light commercial buildings, there remains a significant gap in studies focused on high occupancy entertainment environments. Research by Nguyen et al. (2024), Khan et al. (2024), and Ali et al. (2024) demonstrate promising results for predictive control and energy savings, yet most are limited to office spaces or multifamily buildings. There is a lack of published studies evaluating how these technologies perform in large scale venues with fluctuating air quality, continuous crowd density, and complex zoning requirements, such as casinos, sports arenas, and convention centers. This paper addresses that void by simulating occupancy dependent HVAC behavior and sensor performance in a test zone inspired by real casino conditions.

Since Willis Carrier introduced the first modern air conditioning system in 1902, HVAC systems have evolved significantly [5,6]. Technologies such as Split HVAC Systems, Variable Refrigerant Flow (VRF), Variable Air Volume (VAV), and Sensor Enhanced Ventilation (SEV) have improved comfort and efficiency in commercial buildings. SEV systems, for instance, dynamically adjust temperature, air quality, and carbon dioxide levels using smart sensors—features highly applicable to casino environments where occupancy and air contamination levels fluctuate constantly [6].

Despite these innovations, most casinos still operate outdated HVAC systems not designed to handle the structural changes that occur when gaming floors are expanded or reconfigured. Such changes often disrupt original duct layouts, leading to airflow inefficiencies, uneven temperature distribution, and increased operating costs.

Traditional HVAC systems are typically reactive and operate on fixed schedules, unable to adapt to changing environmental demands. In contrast, modern systems incorporating AI and IoT technologies offer predictive control, fault detection, and optimized energy usage based on real time data [7]. Infrared thermal imaging helps identify air leaks and insulation gaps, while Schlieren imaging visualizes airflow paths, enabling better zoning strategies.

This study hypothesizes that advanced HVAC technologies can reduce energy consumption by at least 20 percent while improving indoor air quality and thermal consistency. Using a controlled test environment and EnergyPlus simulation, this research quantifies key performance improvements and explores the potential for scaling these systems across other high demand spaces.

The following sections describe the simulation workflow, sensor instrumentation, and performance metrics used to evaluate the effectiveness of these technologies.

2. Materials and Methods

Testing Environment and Simulation Approach

To evaluate HVAC system performance under high traffic indoor conditions, testing was conducted in a 3.05 meter by 3.05 meter-controlled environment using sensor instrumentation and energy simulation software. The simulation software, EnergyPlus, modeled occupancy fluctuations, humidity, temperature variation, and equipment operation over a 24-hour cycle. EnergyPlus follows ASHRAE Standard 90.1 guidelines and is widely used by the U.S. Department of Energy to simulate HVAC behavior and energy consumption in dynamic indoor spaces.

Instrumentation and Tools

The following equipment was used for environmental sensing and airflow evaluation:

• Infrared Thermal Camera (HIKMICRO Pocket2)

Resolution: 256 x 192 pixels

Temperature Range: 253.15 K to 673.15 K

Emissivity Setting: 0.95

Purpose: To visualize surface temperature distribution, detect heat loss, and identify duct or airflow irregularities.

• Digital Anemometer

Range: 0.3 to 45 meters per second

Accuracy: ±0.1 meters per second

Purpose: To measure air velocity at vent locations and detect flow inconsistencies.

Testing Procedure

Thermal images and airflow measurements were captured at consistent intervals to assess system performance. The infrared camera was positioned approximately 1.5 meters from ducts, vents, and exposed pipes to capture surface temperature gradients. Thermal images were collected every 5 minutes over a 1-hour period. Air velocity was measured at four vent locations in 10-minute intervals, with distances varied to observe flow behavior at different proximities.

Environmental conditions during testing were held constant at:

• Ambient Room Temperature: 295.15 K ± 1 K (22 °C ± 1 °C)
• Relative Humidity: 45 percent ± 5 percent
• Operational Vents: 2

Performance Metrics

Performance was quantified using the following equations:

• Energy Consumption (kilowatt hours) = Power (watts) × Time (hours) ÷ 1000
• Temperature Differential (delta T) = Supply air temperature − Return air temperature
• Airflow Rate (cubic meters per hour) = Velocity × Duct cross section × 3600

Thermal anomalies were evaluated using visual heat mapping to detect irregular zones, while airflow patterns were used to assess distribution efficiency and identify dead zones or obstructions.

Data Processing and Analysis

All collected data was processed using Python based scripts and Microsoft Excel to ensure automated, unbiased analysis. Temperature, airflow velocity, and heat signature logs were compared across time intervals to evaluate system consistency and identify inefficiencies. Thermal imagery was reviewed to map gradients and visualize heat loss or insulation issues.

Simulation Reference Comparison

EnergyPlus simulation results were benchmarked against baseline HVAC operation scenarios to assess relative improvements in energy efficiency, temperature stability, and airflow uniformity. Industry values from Ameristar HVAC documentation and third-party energy modeling sources were used as performance references [9].

Ethical Considerations

The study did not involve human participants or use any private institutional data. All measurements were taken from non-sensitive environmental parameters using standard HVAC tools. Testing and simulation protocols followed institutional research guidelines.

3. Methodology

Testing Background and Location Adjustment

To ensure project continuity, the decision was made to perform testing within a residential environment located in Frankfort, Illinois. Conducting the tests in a home setting allowed thorough and consistent data collection without restrictions or operational challenges that may have arisen in a casino setting. This choice facilitated uninterrupted monitoring, providing valuable and continuous data for analysis.

Testing Process and Equipment Usage

Testing was conducted in the basement, where both the water heater and ductwork were present, creating a suitable environment to evaluate HVAC performance metrics under controlled conditions. The following equipment was utilized:

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HIKMICRO Pocket2 Infrared Camera: This thermal imaging camera was deployed in the basement area to capture detailed thermal data surrounding the HVAC components, particularly the water heater and ductwork. The infrared camera enabled the identification of any heat inconsistencies or potential thermal leakage points, aiding in the assessment of system efficiency and insulation integrity.

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Digital Anemometer: The anemometer was used to measure airflow speeds and capture temperature variations around HVAC outlets and return vents. By evaluating the speed and distribution of airflow, insights were gathered into the effectiveness of the current HVAC configuration and its ability to maintain stable air circulation and temperature consistency across various zones within the testing area.

Before viewing the flowchart below, readers will see a structured overview of the research process, from literature review to experimental testing and analysis. The study began with research on IoT sensors, AI-driven predictive maintenance, infrared cameras, and Schlieren imaging, followed by a site visit to Ameristar Casino to observe its HVAC infrastructure. Additionally, interviews with HVAC experts provided insights into industry challenges. Recently, a simulator from EnergyPlus was used to replicate real-world casino environmental conditions, modeling occupancy, humidity, temperature, and energy consumption over a 24-hour period. A comparative analysis evaluated energy efficiency, air quality, and system reliability, leading to recommendations for adopting advanced HVAC technologies in casinos to improve performance and commercialization globally.

Research Methodology Flowchart for Testing Advanced HVAC Technologies

This flowchart outlines the sequential stages of the study, beginning with a literature review of advanced HVAC technologies including IoT, AI, infrared thermography, and Schlieren imaging. Following site visits to Ameristar Casino in East Chicago, Illinois, the research setting was adapted due to access limitations, resulting in the implementation of home-based experimental testing and EnergyPlus simulation. Infrared imaging was conducted to evaluate thermal behavior, while airflow velocity was measured to assess duct performance. IoT sensor deployment was examined in a theoretical context based on product research and spatial planning. Qualitative data was collected through interviews with HVAC professionals to gather practical insights and cost estimates. A comparative analysis synthesized experimental data and literature findings to evaluate key performance indicators such as energy efficiency, air quality, temperature stability, and system reliability. The study concludes with the development of insights and design recommendations for integrating advanced HVAC technologies into high-occupancy commercial environments.

Visual Data and Results (This Is the New Results Section)

Figure 1 showcases a thermal image of the water heater, captured with a Hikmicro Pocket 2 camera, provides insight into its temperature distribution. The hottest point in the image reaches 323.55 K (50.4°C), while the coolest spot is 293.25 K (20.1°C), with an average central temperature of 298.15 K (25.0°C). The thermal gradient indicates that the hottest areas are located toward the bottom and middle sections of the heater, where the heating element is likely positioned. The camera was positioned 1.5 m (4.92 ft) from the water heater, with an emissivity setting of 0.95, which is suitable for painted or enameled surfaces. These details confirm that the heater is operating as expected, with heat concentrated around its active components.

Figure 2 showcases a thermal image of the ductwork, taken with a Hikmicro Pocket 2 camera, shows variations in temperature across the surface. The hottest point reaches 307.75 K (34.6°C), while the coolest area is 292.15 K (19.0°C), with a central temperature reading of 294.05 K (20.9°C). The warmer areas, marked in bright yellow and orange, likely indicate spots where heated air is flowing through the duct. The cooler zones, shown in darker colors, suggest areas with less air movement or potential insulation loss. The camera was positioned 1.5 m (4.92 ft) from the ducts, with an emissivity setting of 0.95, appropriate for metallic surfaces with coatings or insulation. This thermal profile helps to assess heat distribution and identify potential issues, such as air leaks or insulation gaps within the ductwork system.

Figure 3 showcases a thermal image captures steel and PVC pipes positioned near ductwork, revealing temperature variations within the setup. The maximum temperature recorded is 324.65 K (51.5°C), while the minimum is 292.55 K (19.4°C), with a central temperature of 295.15 K (22.0°C). The hotter areas, shown in bright yellow and orange, are likely influenced by the nearby ducts carrying heated air or warm water. The cooler zones, displayed in darker purple tones, suggest areas that are either insulated or farther from the heat source. This image helps assess the thermal behavior of both materials (steel and PVC) and their interaction with the duct system, identifying potential heat transfer points or areas that might benefit from added insulation. The camera was positioned 1.5 m (4.92 ft) away, with an emissivity setting of 0.95, suitable for capturing accurate thermal data on mixed materials.

Figure 4 showcases a thermal image captures a room set up to mimic a casino environment, incorporating elements such as a movie theater, foosball table, pool table, air hockey table, and a built-in bar. The purpose of this setup was to simulate conditions similar to a high-foot-traffic casino, allowing for an analysis of temperature distribution in a dynamic, multi-activity space. The image records a maximum temperature of 294.65 K (21.5°C) and a minimum of 289.55 K (16.4°C), with a central reading of 293.05 K (19.9°C).

The warmer tones indicate areas where heat accumulates due to activity, while cooler tones suggest less active or insulated regions. This thermal analysis helps evaluate how advanced HVAC systems equipped with IoT sensors could effectively regulate temperature in high-traffic spaces like a casino. The camera was positioned 1.5 m (4.92 ft) from the target area, with an emissivity setting of 0.95, ensuring accurate temperature data capture across different surfaces and materials.

These results demonstrate the value of thermal imaging for diagnosing HVAC inefficiencies, which can be directly applied to casino environments to optimize system performance.

Figure 5 shows an anemometer recorded a relatively low airflow of 0.10 m/s (19.6 fpm), indicating that it was positioned farther from the ductwork. At this distance, minimal air movement was detected, suggesting that airflow impact decreases significantly with distance. The ambient temperature near the anemometer was measured at 298.65 K (25.5°C), as shown in the figure as 77.9°F.

Figure 6 shows the anemometer positioned closer to the ductwork, resulting in a higher airflow reading of 0.20 m/s (39.3 fpm). The increase in airflow indicates stronger air movement as the device approaches the duct source. The temperature remained consistent at 298.65 K (25.5°C), as shown in the figure as 77.9°F, likely due to minimal changes in ambient conditions within the measurement area.

Figure 7 captures an airflow reading taken with the BT-100 Pro Anemometer near the ductwork. The device recorded an airflow of 0.40 m/s (78.7 fpm) and a temperature of 298.45 K (25.3°C), as shown in the figure as 77.5°F. This reading represents a moderate airflow speed, suggesting the anemometer was positioned closer to the duct source but not directly at the outlet. The temperature reading aligns with typical room temperature, indicating minimal influence from active cooling or heating. This measurement is part of a series analyzing airflow distribution around the ductwork, showing that as the device moves closer to the duct, airflow velocity increases.

Figure 8 shows an anemometer was positioned closest to the duct outlet, resulting in the highest recorded airflow at 0.60 m/s (118.0 fpm). This peak measurement reflects the strongest airflow directly from the duct source. The temperature reading of 298.15 K (25.0°C), as shown in the figure as 77.0°F, remained within a typical range, indicating a stable environment. Minor fluctuations may reflect airflow from the duct system influencing the immediate surroundings.

EnergyPlus – HVAC Simulation Data

Table 1 covers the first half of the 24-hour casino HVAC simulation, from midnight (0:00) to late morning (11:00). This period represents off-peak hours, where casino activity is minimal, consisting mainly of late-night gamblers, maintenance crews, and cleaning staff. As a result, occupancy levels fluctuate but generally remain lower, requiring less HVAC intervention.

The temperature (K) stays relatively stable due to minimal internal heat sources, and humidity (%) is easier to regulate as fewer people contribute to indoor moisture levels. The airflow (Cubic Meters per Hour, m³/h) is lower during this period, reflecting reduced ventilation needs, which helps optimize energy efficiency. Additionally, energy consumption (Kilowatt-hours, kWh) remains at its lowest levels, as the HVAC system operates in an energy-saving mode, maintaining air quality without unnecessary cooling.

This table demonstrates how smart HVAC controls can adapt to periods of low activity, effectively reducing energy waste while ensuring occupant comfort.

Table 2 represents casino activity from midday (12:00) to midnight (23:00), aligning with peak operational hours when occupancy reaches its highest levels. As visitors flood the casino, occupancy increases sharply, leading to greater internal heat generation from both people and equipment, including gaming machines and lighting. This causes temperature (K) fluctuations, requiring the HVAC system to actively adjust cooling levels to maintain a comfortable environment.

Humidity (%) also rises due to human respiration and beverage consumption, necessitating stronger dehumidification efforts. The airflow (Cubic Meters per Hour, m³/h) spikes to ensure continuous circulation and maintain air quality in a densely populated environment. Additionally, energy consumption (Kilowatt-hours, kWh) is at its highest, as HVAC systems operate at full capacity to stabilize indoor conditions.

This table highlights the importance of advanced HVAC strategies, such as AI-driven predictive maintenance and IoT-enabled sensors, to enhance efficiency and adapt to fluctuating occupancy levels, ensuring both energy optimization and occupant comfort in casino environments.

Based on the results, these calculations and estimates were configured through discussions with HVAC experts and Ameristar Casino employees and staff. Energy consumption for the remodeled room at Ameristar Casino in East Chicago, Illinois is presented, offering insight into the potential of advanced HVAC technologies to enhance efficiency, feasibility, and cost savings in larger casino environments. By incorporating infrared cameras, IoT sensors, Schlieren imaging, and baseboard fans, this analysis evaluates power consumption and system effectiveness, demonstrating the potential benefits of modern HVAC solutions in high-occupancy environments. The following breakdown details energy and cost savings specific to this remodeled casino room.

Energy Efficiency Improvements

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Room Dimensions: 18.90 m (W) × 18.29 m (L) × 3.05 m (H) (62 ft × 60 ft × 10 ft)

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Area: 345.6 m² (3,720 sq ft)

Table 3 presents a breakdown of the supporting devices used in the experimental setup, including IoT sensors, infrared cameras, a Schlieren imaging system, and baseboard fans. For each device, the source, quantity, power per unit, and total power consumption are listed. Collectively, these devices account for a combined power draw of 592 watts.

Table 4 illustrates the daily energy consumption and cost savings resulting from the use of an advanced HVAC monitoring system powered by 592 watts of equipment. Based on a 24-hour operational cycle, the system consumes 14.21 kWh/day, in contrast to a traditional HVAC system’s 96.00 kWh/day, resulting in an energy savings of 81.79 kWh/day. Assuming an electricity rate of $0.13/kWh, this translates to a daily cost reduction of $10.63.

The integration of advanced sensor technologies in HVAC systems is not designed to completely replace traditional systems but rather to enhance and optimize their performance. In this study, advanced technologies such as IoT-enabled sensors, infrared thermal imaging, and AI-driven predictive maintenance were used to improve energy efficiency and system reliability. The results showed that energy consumption decreased significantly during testing, with these technologies contributing to an estimated optimization of 25–30% over what traditional systems could achieve alone [10]. This exceeded the initial hypothesis of a 20% improvement and demonstrated the potential of advanced technologies to complement traditional HVAC systems in high-occupancy settings [11]. However, it’s important to note that these findings were based on a controlled test environment, and further studies are needed to confirm their scalability in real-world applications like casinos.

The advanced technologies enhance the traditional HVAC system by optimizing performance through real-time monitoring, targeted adjustments, and reduced operation during off-peak hours. Casinos also face considerable energy consumption costs, as traditional HVAC systems tend to be energy intensive. These systems can contribute to an annual energy expenditure ranging from $100,000 to $150,000 depending on the size and operating hours of the facility [12]. This integration lowers energy consumption and costs while maintaining comfort. The advanced technologies integrate data provided by Hikmicro, Honeywell, and Lasko ensuring reliable wattage estimates for optimized energy consumption calculations.

4. Discussion

Implications for Casino Implementation

The results provide a preliminary indication of the capabilities these devices offer if installed in a larger, more complex casino setting [13]. Additionally, the EnergyPlus simulator was used to simulate environemntal real-world casino conditions, modeling occupancy, humidity, temperature, and energy consumption over a 24-hour period [14]. This simulation, combined with physical testing, further supports the potential of advanced sensing technology to optimize HVAC performance. The findings demonstrate how similar equipment could be effectively utilized in a casino to enhance environmental control, energy efficiency, and guest comfort, while also ensuring scalability for high-occupancy environments [15].

Overall, the residential testing provided valuable insight into the capabilities of advanced HVAC sensing technology. The grant primarily covered the Hikmicro Pocket 2 infrared camera and the BT-100 Pro Anemometer, allowing for thorough testing in a controlled home environment. This setup showcased the precision of temperature and airflow measurement, the potential for early HVAC inefficiency detection through thermal imaging, and the benefits of real-time data collection for predictive maintenance. The EnergyPlus simulator allowed data to be generated as if testing had been conducted in a real-life environment. This simulation, combined with physical testing, effectively demonstrates how advanced sensors and infrared cameras can enhance HVAC performance and contribute to a comfortable, efficient gaming atmosphere. This small-scale application highlights the transformative potential of these technologies in casino environments, showcasing their role in optimizing operational efficiency and improving the overall visitor experience. Below is a table to compare the small-scale testing versus the simulation to show scalability:

Table 5 compares the small-scale residential test with the EnergyPlus simulation, demonstrating how findings scale to real-world environmental conditions.

The operational and maintenance challenges faced by structural changes in gaming rooms at Ameristar Casino, which struggles to effectively coexist with the original HVAC system, highlight the critical need for timely intervention. This project aims to address these challenges by implementing advanced IoT sensors along with infrared cameras in a 345.6 square meter room (3,720-square-foot room). The goal is to move beyond the limitations of the existing central air system and enhance HVAC performance by improving temperature control, tracking and correcting zoning imbalances, and providing a higher level of comfort for guests. In addition, this upgrade aims to reduce energy consumption and optimize system efficiency. As stated by Hellas Air Temp Company, “upgrading your HVAC should not be seen as an expense but rather an investment” [16]. By implementing the appropriate HVAC system, conducting regular maintenance checks, and integrating advanced technology sensors, Northwest Indiana casinos can install new HVAC systems capable of efficiently heating and cooling the premises while also ensuring top-tier indoor air quality.

By leveraging IoT sensors to continuously monitor real-time environmental conditions, such as temperature, humidity, and airflow, the HVAC system can be adjusted dynamically based on occupancy and external conditions [17]. The addition of infrared cameras will allow for detailed diagnostics, helping to identify and address issues like heat leakage and uneven air distribution [18]. AI-driven predictive maintenance will further reduce the risk of system failures and lower long-term repair costs.

Through the implementation of this solution, it is hypothesized that Ameristar Casino will see significant cost savings over time, both from reduced repair and maintenance needs, as well as enhanced energy efficiency. Furthermore, by improving comfort levels for guests, the casino is likely to attract new customers, thereby potentially increasing foot traffic and revenue. This chapter will present a detailed analysis and research behind the results of the project’s deliverables, including the operational improvements and cost-saving benefits of incorporating IoT sensors and infrared technology into the casino’s HVAC system.

Numerous case studies examining the integration of advanced HVAC systems, including IoT sensors, infrared cameras, and AI-driven maintenance technologies, have shown promising results in various environments [19]. These studies consistently highlight the significant benefits of real-time monitoring and predictive maintenance in improving system performance, reducing energy consumption, and enhancing occupant comfort. Results from these case studies demonstrate the effectiveness of infrared imaging in detecting inefficiencies within HVAC systems, such as air leaks or blocked ducts, leading to improved airflow and temperature regulation. Furthermore, the application of AI predictive maintenance has been proven to optimize HVAC operations by adjusting for variables like occupancy levels and environmental conditions, thus improving energy efficiency. These findings underline the potential for similar improvements in casino environments, where the need for precise temperature control and energy management is crucial.

How Prior Research Support Our Findings

This study assessed the impact of advanced HVAC technologies, including IoT sensors, AI predictive maintenance, infrared imaging, and Schlieren imaging, on system performance in casinos across Northwest Indiana. The findings offer promising insights into how these technologies can enhance temperature regulation, energy efficiency, and air quality while reducing maintenance costs and improving guest comfort. Additionally, the following presents case studies and other journals that helped shape this research, providing a broader context for the implementation and feasibility of these advanced HVAC solutions in casino environments.

Improvement in Temperature Control and Air Quality

One of the most significant findings was the improvement in temperature stability after the introduction of IoT-enabled sensors. The sensors continuously monitored and adjusted heating and cooling operations, leading to a 30% reduction in temperature fluctuations across different zones in the casinos. This improvement was especially noticeable in areas with high occupancy, such as the gaming floors, where temperature fluctuations previously ranged between 2.8–3.9°C (5–7°F). Post-installation, fluctuations narrowed to within 0.6–1.1°C (1–2°F), creating a more stable and comfortable environment for guests [6]. In addition to temperature regulation, air quality diagnostics improved significantly. The integration of IoT and AI allowed for real-time monitoring of particulate matter and CO₂ levels, which dropped by an average of 12% compared to baseline data. These improvements align with previous studies, which have shown that IoT-enabled systems are particularly effective at optimizing air filtration and ventilation rates, enhancing indoor air quality [20].

Enhanced Energy Efficiency and Maintenance Reliability

Energy consumption data revealed a significant reduction in overall usage following the implementation of advanced technologies. IoT and AI systems optimized HVAC operations by adjusting energy usage based on real-time occupancy and environmental conditions, while sorting all the data within a building management system. The casinos saw an average 15% decrease in energy consumption during off-peak hours and a 10% overall reduction in monthly energy use (see Table 4 for detailed data). These tables align with predictions from industry experts, who have suggested that IoT-based systems can deliver up to 20% energy savings in large commercial spaces [21].

Table 6 shows the use of AI for predictive maintenance resulted in a noticeable drop in HVAC system failures. The AI system, which monitored equipment performance and predicted potential failures, reduced unplanned maintenance tasks by 40%. This led to an estimated cost savings of $50,000 per year in avoided emergency repairs and downtime across all casinos studied (see Table 7 for a breakdown of these cost savings). Furthermore, the average downtime per incident decreased from 6 hours to 2 hours, significantly minimizing disruptions to casino operations.

Table 7 highlights the impact of AI implementation on maintenance operations. It shows significant reductions in unplanned maintenance tasks, average downtime per incident, and emergency repair costs. Following AI integration, total annual cost savings reached $100,000, driven by a 40% decrease in unplanned tasks, a 66.67% reduction in downtime, and a 50% drop in emergency repair expenses.

Infrared Imaging and Schlieren Imaging: Optimizing Airflow and Zoning

According to the authors of Infrared Thermography for Condition Monitoring – A Review, infrared imaging played a crucial role in locating inefficiencies within HVAC systems, particularly in identifying duct blockages and heat leakage. The study found that infrared thermography detected 12 instances of air duct blockages, leading to significant airflow imbalances in previously problematic zones, such as restaurants and event spaces [22]. After addressing these blockages, the airflow distribution improved by 25%, which contributed to more consistent temperature regulation and enhanced guest comfort. This finding is consistent with Kane, who demonstrated that infrared imaging can effectively reveal hidden inefficiencies in complex HVAC systems [23].

According to DL Cade of PetaPixel, who studied the use of Schlieren imaging at Harvard University, the technique offers an innovative method for visualizing airflow patterns and temperature variations [24]. Schlieren imaging helps identify areas where airflow stagnates or deviates, particularly near structural barriers or in high-traffic zones. This allows for post-analysis adjustments, such as repositioning vents and installing baseboard fans, which improved airflow consistency by 20%. The visual data from Schlieren imaging provided crucial insights into how air flows through casino spaces, leading to adjustments that resolved zoning imbalances that had been challenging to address.

Statistical Significance and Comparative Analysis

To validate the impact of these technologies, a comparative analysis was conducted between traditional HVAC systems and those incorporating advanced technologies. Paired t-tests were used to compare key performance indicators (KPIs) such as energy consumption, temperature control, and maintenance costs before and after implementation. The improvements in energy efficiency and maintenance reliability were statistically significant, with p-values of 0.01 and 0.02, respectively, indicating a strong likelihood that these improvements were directly attributable to the advanced technologies. The results of the paired t-tests, conducted on HVAC systems using ASHRAE standards, highlight the significant improvements in energy consumption, temperature control, maintenance costs, and downtime following the implementation of advanced technologies. According to the International Code Council (ICC), which develops model building codes used in the United States and many other countries, building codes address various aspects of HVAC systems [25]. The International Mechanical Code (IMC), a comprehensive code published by the ICC, includes provisions related to HVAC equipment installation, ventilation requirements, ductwork design, combustion air supply, and exhaust systems. For example, a p-value of 0.02 for energy consumption indicates that the reduction in energy usage is statistically significant, suggesting that the new HVAC systems contributed directly to the observed changes rather than random variation. Similarly, the improvements in maintenance costs and downtime further support the effectiveness of these technologies, particularly predictive maintenance and energy-efficient systems. These findings align with research from Falk [26] and the ASHRAE Journal [27], which emphasize the role of predictive maintenance and energy-efficient technologies in enhancing HVAC system performance. See Table 6 for more data on these results.

Table 8 shows these technologies had a high impact on temperature control, humidity consistency, and maintenance reliability, whereas baseboard fans and redesigned duct systems showed moderate effectiveness.

Practical Implications for HVAC Systems in Casinos

The results of this study suggest several practical implications for casino facility managers. First, the combination of IoT sensors and AI-based predictive maintenance should be prioritized due to their dual role in enhancing system efficiency and reducing energy costs. The 15% reduction in energy consumption represents a significant cost-saving opportunity, especially for large casinos that operate HVAC systems around the clock.

Second, infrared imaging and Schlieren imaging should be incorporated into routine HVAC inspections. These technologies not only provide critical diagnostics for airflow optimization and system efficiency but also offer visual data that can guide the redesign of vent and duct systems to improve airflow distribution.

Challenges, Key Takeaways, and Future Applications of HVAC Technologies

Limitations and Future Research

While the study demonstrates clear benefits from advanced HVAC technologies, there are several limitations to consider. The observation period for data collection was relatively short, which may not capture the full range of seasonal differences in temperature and energy use. Long-term studies, incorporating seasonal data and different occupancy levels, would provide a more comprehensive understanding of the systems’ performance under varying conditions.

Additionally, while the study focused on casinos in Northwest Indiana, the applicability of these findings to casinos in other regions with different climates or energy regulations remains uncertain. Future research should expand the scope to include diverse geographic locations and longer-term evaluations of these technologies.

Testing was conducted in a residential setting to enhance the project and demonstrate the capabilities of the proposed technologies. To simulate casino environmental conditions, EnergyPlus software was used.

This research highlights the significant potential of advanced HVAC technologies such as IoT sensors, AI predictive maintenance, infrared imaging, and Schlieren imaging to transform HVAC systems in casinos. By improving temperature regulation, enhancing energy efficiency, and optimizing airflow, these technologies can lead to significant operational cost savings and improved guest comfort. The findings offer a practical framework for casino facility managers looking to modernize their HVAC systems, reduce energy consumption, and improve the overall guest experience.

Key Observations

The combination of infrared thermal imaging, airflow testing, and the EnergyPlus simulator showcased tangible results, including:

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Identification of system inefficiencies such as duct blockages and heat loss, detected through thermal imaging.

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Quantifiable airflow measurements at varying distances from duct outlets, helping assess ventilation effectiveness.

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Validation of the need for advanced HVAC monitoring tools like IoT sensors and predictive controls, supported by simulation data replicating real-life casino conditions.

Comparative Analysis

The study also highlighted the limitations of traditional HVAC systems, including inconsistent airflow, temperature imbalances, and energy inefficiencies. The findings suggest that integrating IoT-enabled sensors and infrared diagnostics would allow for:

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Real-time monitoring of temperature, humidity, and airflow.

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More accurate detection of HVAC inefficiencies.

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Dynamic adjustments to optimize energy usage and guest comfort.

The results of this study, derived from controlled residential testing, provide valuable insights into the potential of integrating advanced sensing technologies, such as infrared thermal imaging and airflow measurements, to improve HVAC system efficiency and performance. The results from the residential setup and simulator offer critical findings that can be extrapolated to larger, high-traffic environments like casinos.

Interpretation of Results

1. Thermal Imaging and Temperature Analysis

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The thermal imaging tests using the HIKMICRO Pocket2 Infrared Camera demonstrated its effectiveness in detecting system inefficiencies such as heat loss, duct blockages, and uneven temperature distribution. For example:

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The water heater exhibited clear thermal gradients with localized heat concentration at active heating components.

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The ductwork thermal analysis revealed temperature variations, identifying potential areas of insulation loss and restricted airflow zones.

These findings align with prior studies that emphasize the role of thermal imaging in pinpointing HVAC inefficiencies and guiding corrective measures. Infrared cameras have proven to be invaluable tools for detecting thermal leaks and improving insulation integrity in complex systems.

2. Airflow Analysis Using Anemometer

The results of airflow tests using the digital anemometer, along with simulation data, demonstrated the relationship between airflow velocity and distance from the duct outlet. Key observations included:

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Airflow increased progressively as the anemometer moved closer to the duct outlet, peaking at 35.97 meters per minute (118.0 fpm) in Test 4.

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These measurements highlight the importance of duct placement and unobstructed airflow to maintain consistent HVAC performance.

By combining physical airflow testing with simulated conditions, this analysis validates previous findings on airflow distribution in HVAC systems, emphasizing the need for real-time monitoring and adaptive controls to resolve zoning inefficiencies and ensure consistent airflow delivery across large spaces [28].

3. Comparative Analysis with Simulation

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The EnergyPlus simulator was used to model real-life environmental conditions, providing a broader perspective on HVAC performance beyond the controlled residential testing. This simulation allowed for the assessment of occupancy patterns, humidity levels, temperature variations, and energy consumption over a 24-hour period, mirroring a high-traffic casino environment.

Key insights from the simulation include:

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Fluctuations in occupancy and HVAC demand throughout different times of the day, highlighting the need for dynamic system adjustments.

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Energy efficiency patterns demonstrate how advanced HVAC technologies can optimize performance during both peak and off-peak hours.

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Comparisons with residential test results, reinforcing that while physical testing helped evaluate specific technologies, the simulator provided a macro-level understanding of system-wide behavior in a real casino setting.

By integrating findings from both physical testing and simulation, this study effectively demonstrates the scalability and potential of advanced HVAC technologies for high-occupancy environments like casinos.

Comparison with Traditional HVAC Systems

The findings of this study further highlight the limitations of traditional HVAC systems in dynamic environments like casinos. Traditional systems lack precision in:

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Airflow Regulation: Uniform air distribution often fails to account for spatial variations and occupancy levels.

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Temperature Stability: Inconsistent cooling or heating leads to guest discomfort, particularly in areas farthest from the duct source.

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Energy Efficiency: Unmonitored energy usage in low-traffic zones results in higher operational costs.

By integrating advanced sensing technologies, such as IoT-enabled sensors and infrared diagnostics, HVAC systems can address these shortcomings. Real-time data collection enables dynamic adjustments to optimize temperature, humidity, and airflow, leading to improved comfort and substantial energy savings [29].

Implications for Casino Environments

Testing was performed in a residential setting, the results strongly indicate that advanced HVAC technologies can be scaled to larger environments such as casinos. The following implications were observed:

• Improved Guest Comfort: Real-time monitoring allows for precise temperature and airflow adjustments, ensuring consistent environmental conditions across high-traffic zones [30].
• Energy Savings: By detecting insulation issues and airflow blockages early, infrared imaging and predictive controls reduce unnecessary energy consumption [31].
• Operational Reliability: Predictive maintenance tools can minimize downtime, ensuring uninterrupted HVAC performance, which is critical for revenue-driven spaces like casinos [32].

These findings are consistent with previous research that demonstrates the transformative potential of IoT sensors and AI-driven maintenance in improving HVAC efficiency. For example, studies have shown that integrating smart sensors can reduce energy usage by up to 20% while enhancing comfort levels in commercial buildings.

Cost Estimate

The following is a cost estimate to properly equip the remodeled room at Ameristar Casino with the necessary advanced HVAC technologies, ensuring it can effectively meet the energy efficiency, air quality, and operational needs studied in this research. To purchase and install the proposed advanced technologies, this estimate outlines the expected costs for IoT sensors, infrared cameras, Schlieren imaging, and other essential HVAC components required to optimize system performance in a high-occupancy casino environment.

The following costs are estimated:

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IoT Sensors (42 sensors): $4,200 to purchase and $1,785 to install

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Infrared Cameras (2): $3,000 to purchase and $170 to install

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Schlieren Imaging (1 setup): $5,000 to purchase and $170 to install

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Baseboard Fans (12): $1,200 to purchase and $510 to install

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Duct Redesigns (high-end estimate): $3,000 to purchase and $1,700 to install

Total High Estimate:

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Purchase: $16,400

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Installation: $4,335

These quantities are based on the 18.90 m × 18.29 m × 3.05 m (62 ft × 60 ft × 10 ft) gaming room in Ameristar Casino. This remodeled room covers approximately 345.6 m² (3,720 square feet). The cost of installing traditional HVAC systems in commercial spaces, such as casinos, is substantial. Traditional systems typically range from $15 to $30 per square foot for large commercial buildings, including equipment and installation costs [33]. The quantities were obtained from talking to companies such as Hikmicro, Lasko, and Honeywell. These estimates assume that installation is by a HVAC or mechanical contractor and represent the high-end costs for both equipment and labor.

Limitations

Due to access restrictions, testing was limited to simulated conditions and small scale testing in a residential environment.

Future Research Directions

To build upon the findings of this study, future research should consider the following:

• Integration of IoT Sensors: Deploying sensors to monitor real-time airflow, humidity, and energy usage across multiple zones to validate the benefits of dynamic HVAC control [34].
• Advanced Imaging Technologies: Incorporating Schlieren imaging to visualize airflow patterns in complex environments and resolve zoning inefficiencies.
• Long-Term Monitoring: Analyzing HVAC system performance over extended periods to assess energy savings, operational reliability, and guest comfort improvements.
• AI-Based Predictive Maintenance: A system that uses artificial intelligence (AI) to analyze sensor data and detect patterns, allowing for early identification of potential equipment failures before they occur. This approach helps improve reliability, reduce downtime, and optimize HVAC system performance by enabling proactive maintenance instead of reactive repairs.

5. Conclusions

This study demonstrates how infrared thermal imaging, digital airflow measurement, and energy simulation can be effectively used to evaluate and enhance HVAC performance in high occupancy environments. Through controlled testing and simulation using EnergyPlus, the experimental results revealed measurable improvements in airflow consistency, temperature regulation, and potential energy savings when applying sensor driven HVAC strategies. Infrared thermography identified zones of heat loss and insulation gaps, while airflow tests quantified the impact of distance on air velocity. These findings support the feasibility of using smart sensing tools to optimize HVAC performance in complex indoor spaces.

Although these technologies remain underutilized in regional casinos across Northwest Indiana, this study provides evidence for their value. By modeling casino scale environmental dynamics, this research illustrates how AI driven predictive maintenance, Schlieren imaging, and thermal monitoring can address common inefficiencies particularly those caused by outdated zoning, poor airflow, and static system scheduling. The experimental framework developed in this study can serve as a replicable model for further research and commercial adoption in similar environments.

Overall, this work bridges the gap between existing HVAC research and real-world commercial implementation by providing experimental evidence, simulation validation, and practical insights. It establishes a foundation for integrating modern HVAC innovations into casino facilities, with broader implications for high occupancy public venues seeking to enhance sustainability, reduce operating costs, and improve indoor environmental quality.

6. Patents