AI takes on growing role in HVAC system efficiencies
The push to reduce CO2 emissions makes energy efficiency in buildings a priority.
This article is part four of our 'Match Made in Automation' Series. You can find the other installments of this series below:
In most countries, buildings account for about 40% of the total energy consumed. HVAC and lighting consume around half that amount. Fortunately, AI is already delivering improved energy efficiency in these systems.
AI in HVAC operations and maintenance
Buildings are complex, dynamic environments with a thermal flow that is constantly changing due to variations in occupancy and weather. Optimizing the performance of a heating, ventilating and air conditioning (HVAC) system requires both characterizations of the energy flow and, ideally, the ability to predict how that flow is likely to change. The number of data points and the complexity of modeling equations needed to ensure optimal performance of an HVAC system under all conditions stretch the capabilities of traditional energy management systems. Artificial intelligence (AI) is a powerful tool for modeling these energy flows.
One of the fundamental ways AI contributes to the performance of HVAC systems is through intelligent data analysis. AI algorithms classify, group and analyze vast amounts of data to identify patterns. The data includes temperature, humidity, occupancy and weather conditions. Energy tariffs vary with the time of day, so this is another factor that may be considered. Data processing is carried out on-premises, in data centers in the cloud, at the edge (near the system sensors), or using some combination of these resources.
A simplified architecture of a typical HVAC system is shown in the block diagram.
HVAC control system diagram
A typical AI-assisted HVAC control system maximizes efficiency and comfort.
By analyzing historical patterns and real-time inputs, AI can identify trends, predict demand and adjust HVAC settings, ensuring optimal comfort levels while minimizing energy consumption. This kind of dynamic optimization helps eliminate energy waste, fine-tunes system settings and can be integrated with other building management systems for comprehensive energy management.
AI-based predictive maintenance is a game-changer
Traditional maintenance practices often rely on fixed schedules or reactive responses to failures, leading to inefficiencies and unexpected downtime. With AI, sensors and data from HVAC systems can be continuously monitored, allowing predictive algorithms to identify potential issues before they escalate. By analyzing performance patterns and detecting anomalies, AI can proactively schedule maintenance to prevent critical failures, maximizing system uptime and reducing costs.
AI also simplifies the detection of faults and diagnoses of HVAC system issues, which can be complex and time-consuming. By analyzing system data and comparing it against predefined thresholds, AI algorithms can identify deviations and alert maintenance personnel about potential problems. This early detection enables quick troubleshooting, reducing downtime and preventing further damage. Moreover, AI can offer insights into the root causes of failures, facilitating effective problem resolution.
Maintaining optimal indoor air quality
AI enhances indoor air quality management in HVAC systems by monitoring and analyzing air quality parameters such as CO2 levels, particulate matter and volatile organic compounds. By integrating AI with ventilation systems, the HVAC system can dynamically adjust ventilation rates based on real-time air quality data, ensuring a healthy and comfortable indoor environment while minimizing energy waste. Based in the US, UL, a global safety science company, runs a Verified Healthy Building program to support efforts to improve indoor environments.
Real-world outcomes of AI in HVAC implementations
Although the use of AI in HVAC systems is relatively new, some real-world examples of its success are emerging.
As reported by the International Society of Automation, in 2020, Yokogawa used AI to reduce energy consumption at its semiconductor sensor manufacturing plant in Miyada-mura, Japan. The AI model employed a reinforcement learning algorithm in operating an HVAC system for the company’s clean room fabrication environment. The goal was to maintain tightly regulated environmental conditions while minimizing energy use. The clean rooms at the facility accounted for 30% of its total energy consumption, fueled by liquefied petroleum gas. The AI system implemented by Yokogawa complemented the existing HVAC infrastructure and conventional process controls, so minimal capital investment was needed. The result was a 3.6% reduction in energy consumption after several months of self-driven, model refinement.
In one of several case studies on its website, BrainBox AI, an HVAC company in Montreal, Canada, describes how its AI technology was used to convert a 509,612-square-foot shopping center’s traditional HVAC system into an autonomous one. In this case, custom-curated algorithms running in the cloud combined building data mapped over several weeks with external weather and energy-tariff data to optimize system operation. Electricity savings were 21% (205,214 kWh) after one year, with AI enabling the required building-zone temperatures to be accurately maintained with greatly reduced runtimes for the HVAC equipment.
A summary of the runtime reductions is shown in the table.
| Average runtime reduction by HVAC equipment type |
| SUPPLY FAN RUNTIME |
-33.50% |
| HEATING STAGE RUNTIME |
-62% |
| COOLING STAGE RUNTIME |
-5% |
| REHEAT STAGES RUNTIME |
-30% |
| GLOBAL REHEAT UTILIZATION |
-78% |
| OVERALL FAN RUNTIME |
-61% |
| HEATING MODULATION RUNTIME |
-91% |
| HEATING STATE RUNTIME |
-81% |
Figures show analysis of the runtime reduction for HVAC components achieved by applying AI technology to a 509,612-square-foot shopping center in Canada. (Source: BrainBox AI)
Santagostino, a company operating 35 medical centers across Italy, opted to develop its own AI implementation using the Arduino Nano MCU platform to help with the predictive maintenance of its HVAC systems. The case study, detailed on the Arduino website, demonstrates that AI implementations need not be expensive and that using standard, off-the-shelf components that run on open-source software offers a level of flexibility that dedicated systems may lack. The Arduino Nano RP2040 Connect platforms with onboard Wi-Fi and Bluetooth connectivity run AI algorithms that read and analyze system parameters. The compact Arduino boards were housed in 3D-printed cases and placed inside heat pumps, air conditioning units and ventilation systems across Santagostino’s sites.
The platforms’ integral accelerometers record machine parameters every 30 seconds and detect vibrations. In addition to instantly identifying and locating breakdowns, ongoing data analysis enables maintenance schedules to be optimized, minimizing costs and downtime, and optimizing the hospitals’ environments for patients, visitors and staff. [Buy Arduino products here.]
Conclusion
AI brings remarkable advancements to HVAC systems. The advancements include intelligent data analysis, predictive maintenance, energy optimization, fault detection and diagnostics, indoor air quality management, and continuous improvement through machine learning.
By leveraging AI, organizations can improve comfort levels, reduce energy consumption, enhance system performance and increase sustainability. As AI continues to evolve, the future of HVAC systems holds great promise for innovation and efficiency in the built environment.
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AI Takes on Growing Role in HVAC System Efficiencies | Avnet Silica
