Explainable AI meets HVACR: How Digitalisation Ensures Optimal Performance at Every Operation

As industries embrace digitalisation, Artificial Intelligence (AI) is transforming operations across many sectors and industries. Heating, Ventilation, Air Conditioning, and Refrigeration (HVACR) systems are no exception. With economic growth and climate change driving demand for energy efficiency, the whole HVACR industry is changing.

HVACR systems are becoming increasingly business-critical, impacting cold chains, indoor climate, industrial processes, and heat pumps. The acceptance of AI in this critical infrastructure increases, as it can be a valuable tool to reduce the increase in energy consumption. As HVACR systems are complex, and each system is virtually unique, it is crucial to understand what triggers an alert (or breakdown) before taking action.

This is where Explainable AI (XAI) comes into play, ensuring that Data-Driven energy optimisation in HVACR systems are understandable and actionable. By leveraging the ClimaCheck method, businesses can unlock higher efficiency, reliability, and sustainability while maintaining full visibility into system performance.

What is Explainable AI?

Explainable AI (XAI) is a set of techniques and methodologies that provide insights into how AI models make decisions. Unlike traditional AI, which functions as a “black box” with little visibility into its reasoning and relies heavily on large amounts of data, XAI offers clear, interpretable explanations that help technicians, facility managers, sustainability experts, and business owners make informed decisions.

In the context of HVACR systems, XAI is particularly valuable because:

• It ensures data-driven energy optimisations align with operational goals.
• It builds trust in automated systems by offering clear justifications for recommended actions.
• It enables facility managers to verify and validate AI recommendations before implementation.
• It enhances predictive maintenance and automated fault detection and diagnostics (AFDD) to the next level.

The Need for Explainable AI in HVACR Systems

While the refrigerant process follows well-established physical principles, HVACR systems are highly complex, with multiple interdependent components influenced by various factors. The beauty of this is that efficiency can be calculated for each component, and each should function optimally to ensure overall efficiency and reliability.

Traditional AI can often identify patterns and based on that try to predict failures. However, in many cases this is done without explainability as it is not based on the performance of each

component. The fact that there are rarely two identical systems creates indefinite combinations of operating conditions. This leads to a challenge where decision-makers do not have information required to understand why certain recommendations are being made which makes optimisation in efficient.

For example:

• When an AI supported system identifies a loss of performance, the responsible engineer/technician needs to know if this is caused by a change in operating conditions, refrigerant loss, fouling in the evaporator, or something else to take corrective action.
• When an anomaly is detected in energy consumption, it is essential to determine whether it is due to an actual equipment issue or simply a change in environmental conditions or load.

By implementing Explainable AI, HVACR professionals avoid chasing false alerts and can swiftly identify root causes. This is required to gain the confidence needed to adopt data-driven energy optimisations and predictive maintenance. (here you can download our free guide and read more about this)

The ClimaCheck Method: A Transparent Approach to AI in HVACR

The ClimaCheck method provides a structured approach to HVACR performance analysis by using component-level insights. Since each part of an HVACR unit has a known performance at each operating condition, AI can assess whether the system is operating within acceptable parameters and identify inefficiencies long before they lead to failures.

How ClimaCheck and AI Work Together:

1. Performance and Energy Baselines: AI continuously compares real-time data against the component performance of a digital twin—a virtual replica of the system that reflects expected component behaviour.
2. Automated Fault Detection and Diagnostics (AFDD): If the operation any component, e.g. a compressor, condenser, or evaporator, deviates from its expected efficiency, AI flags it for analysis.
3. Actionable Information: Rather than just issuing alerts, Explainable AI provides the detailed information behind the flagged deviations, helping decision-makers determine whether adjustments, repair, replacement, or maintenance are required.

This approach ensures that HVACR units maintain optimal performance at every operating condition by identifying inefficiencies before they escalate into costly failures and downtime.

You can watch this webinar for free, to learn more about ClimaCheck and AI

Real-World Impact: Data-Driven Energy Optimisation and Predictive Maintenance to maintain HVACR Efficiency

A well-optimised HVACR system not only reduces energy consumption but also enhances reliability and longevity. Here’s how your organisation can benefit from Explainable AI and the ClimaCheck method:

• Energy Savings: It is possible to detect inefficiencies leading to overconsumption and patterns connected to this. You will also have all the information to perform corrective actions, such as optimising controls, flows, refrigerant charge, fan speeds, depending on the problem.
• Predictive Maintenance: Instead of relying on scheduled (preventive) maintenance visits and “waiting for” breakdowns, the condition of each component determines when actions are required, reducing downtime and costs by optimising maintenance.
• Regulatory Compliance: Transparent analytics make it easier to justify decisions, track performance, and ensure compliance with environmental and energy efficiency regulations.

A case from the field:

ClimaCheck online detected a performance deviation, and the facility manager overseeing the HVACR, was noticed. Thanks to the ClimaCheck method and usage of a digital twin, the cause for the deviation is detected and service is scheduled. In this case, the savings were approximately 7%. Because the performance drift was noticed early, the cost to fix the problem was minimal. However, this saving was done after optimisation where the system reached a 50% saving,  further emphasising the importance of continuous performance monitoring and AFDD, to avoid performance drift.

This transparency enables a data-driven process, optimising energy consumption without wasting time chasing false alerts in dynamic systems with continuously changing loads and operating conditions.

The Future of Explainable AI in HVACR

Digitalisation, ClimaCheck online, AI, and Industry 4.0 continue to evolve, pushing the limits of what’s possible in data-driven energy efficiency and optimisation. At the same time, regulations are becoming stricter, increasing the demand for documentation as well as transparency. This combination offers many exciting future possibilities for the ClimaCheck method further enhanced with XAI, such as:

• Enhanced Visualisation Tools: Graphical dashboards that clearly explain XAI-driven insights, making it even easier for technicians and facility managers to understand system performance at a glance.
• Automated Root-Cause Analysis: ClimaCheck online further improve the pinpointing of specific issues with explanations.
• Regulatory Compliance and Enhanced Automated Reporting: possibility to improve the already available AI-generated Energy report. Helping business comply and meet environmental and operational regulations with minimal manual input.

And these are just a few of the possibilities, as technology advances, the role of Explainable AI in HVACR will expand, offering greater insights, efficiency, and reliability while ensuring full transparency and compliance with industry standards.

Conclusion

Explainable AI is a game-changer as helps in the transformation of maintenance from reactive to predictive. Therefore, the whole HVACR industry can enhance efficiency, reliability, and operational transparency. As businesses navigate the shift toward digitalisation, leveraging the ClimaCheck method ensures a future-ready, data-driven approach to optimising performance. It will also be crucial for companies managing a HVACR systems to streamline workflows and use their workforce efficiently.

The ClimaCheck method, combined with XAI, ensures data-driven decision-making with transparency, eliminating the risks of traditional AI black-box solutions.

Ready to optimise your HVACR systems? Contact us today to learn how the ClimaCheck method and Explainable AI can improve your operations.