Using AI To Enhance HVAC Systems-Is It Good?

Central HVAC units have been the primary choice for maintaining temperature in homes and workplaces for many years. While they are effective in temperature regulation, the subjectivity of comfort becomes an issue in large workspaces with multiple occupants. To address this, Personal Comfort Systems (PCSs), including heated seating and fans, can be employed to cater to individual needs.

However, the effectiveness of PCSs is limited due to a lack of communication between the central HVAC and individual PCS units. These systems provide a constant temperature without considering changes in the external environment, requiring manual adjustments. To bridge this integration gap, the utilization of AI and the Internet of Things (IoT) is proposed. This article explores potential models for integrating PCSs with central HVAC systems using AI.

Search Algorithms

Search algorithms play a crucial role in finding optimal solutions by determining the shortest route to a problem. In the context of HVAC, these algorithms can identify the best operational time to maximize comfort by intuitively cooling or heating the space before work hours commence. This approach takes into account people’s preferences by establishing a set of parameters that offer optimal comfort for everyone. For instance, the algorithm can learn from manual adjustments made throughout the day and incorporate those changes into an automated system.

Logical Inferences

While search algorithms can automatically adjust the central HVAC, not all of the resulting solutions would intuitively make sense for human comfort. Computers operate based on binary TRUE and FALSE logic, whereas human reasoning is more advanced.

AI must make decisions based on more complex logical inferences. For example, if the outside temperature drops at night, the AI should determine the new temperature that the HVAC should aim for and under what circumstances this answer should differ. This automation eliminates the need for human input.

Machine Learning

Machine learning, a subfield of AI, involves interpreting data to create models that simulate human brain functions.

Accurate data interpretation is crucial to ensure that the AI-controlled heating or cooling of the HVAC does not overstate or understate the desired temperature in a workspace. An HVAC system must be able to make educated decisions based on the aforementioned logical inferences. By utilizing machine learning, it becomes possible to train a model that adjusts the parameters of the HVAC as if it were an actual human.

Connected Systems

PCSs are employed to create customized environments based on individual preferences. For example, if the air conditioning in a space feels too cold, individuals can use smart foot or seat warmers to achieve a more desirable temperature. With the integration of AI, manual settings contribute to the AI’s training, enabling automatic activation and deactivation based on learned preferences over time.

To account for the dynamic external environment, these PCSs need to be connected to the central HVAC system. For a successful algorithmic approach, all devices must be linked to each other and controlled by a singular AI to ensure accurate control. Connecting PCSs to the HVAC through the internet or local LAN can facilitate this integration.

Challenges And Drawbacks

Implementing such an advanced system poses various difficulties. The system requires significant time and memory resources. Additionally, PCSs are designed to be cost-effective devices that consume less power than HVAC units while providing individual comfort. Incorporating AI functionality would contradict this purpose due to higher manufacturing and operating costs.

Furthermore, algorithms rely on data from more accurate sensors to function as intended, adding to the cost of PCS or HVAC systems. Inexpensive replacement pump parts and sensors are more cost-effective for traditional HVAC systems. On the other hand, an AI-powered solution would require more financial investment for repairs.

In Conclusion

While current research on automatic control is promising, the large-scale implementation of such systems remains to be seen. However, the decreasing costs of computer parts and advancements in cost-effective manufacturing techniques may make AI-integrated HVAC systems a norm in the near future.