Developing a Dynamic HVAC Control Algorithm: A University Conference Room Case Study and Review of Barriers to Adoption
Neale, Caleb, School of Engineering and Applied Science, University of Virginia
Heydarian, Arsalan, EN-Eng Sys and Environment, University of Virginia
Wayland, Kent, University of Virginia
With 30% of commercial energy usage being attributed to heating, ventilation, and cooling (HVAC) operation and a rise in public perception of air quality and cleanliness due to the SARS-CoV-2 pandemic, there exists a dual sided need to produce cleaner air and reduce the cost at which clean air is produced. This portfolio seeks to address this problem by answering firstly whether it is possible to reduce energy demands from HVAC systems through an intelligent, occupancy-based control algorithm, and secondly what barriers to adoption does such a control algorithm or other produced technological solutions face once developed.
An occupancy-based control algorithm was chosen as the technical portion of this portfolio as it offered an opportunity to leverage in-pace hardware for significant energy usage reductions while maintaining indoor air quality (IAQ) during the times in which occupants were present. Other considered solutions required significant hardware upgrades or leveraged complex software interactions between models and HVAC equipment frequently not supported natively; a model which determines simply whether or not HVAC equipment should operate at a given time is simple to implement and easily testable, making it a good first step in addressing the dual-sided IAQ and energy problem of HVAC systems. Such an algorithm was developed using a literature review on best practices for IAQ and tested using data from UVA’s LinkLab. Results showed a monthly energy savings of $424/month when tested on a single conference room in UVA’s LinkLab, though calculated loss in productivity due to degraded IAQ was $522/month. Future work is needed to further refine notions of lost productivity to IAQ and develop an accurate enough occupancy model to maintain energy reductions while maintaining IAQ.
To understand how such a developed control algorithm might see use in the real world, the STS thesis in this portfolio seeks to understand the factors which influence suboptimal adoption of energy efficient technologies generally, with specific effort made to discover factors specific to HVAC technologies and contextualize their adoption with an exploration into electric vehicles and the adoption of LED lighting. The paper determines that both market-based (public good, principal agent) and non-market based (decision making under uncertainty, information dissemination, qualitative factors, optimality definitions) failures contribute to suboptimal adoption rates of energy efficient technologies generally and HVAC technologies. The literature review also discovered geographic dissemination factors specifically applicable to HVAC and residential energy efficiency technologies. The paper determines that further research is needed to quantify the relative effects of each of these barriers and determine which should be addressed with highest priority, though it is immediately apparent that the removal or mitigation of any of the addressed barriers would significantly increase adoption rates of an efficient HVAC technology.
The work of this portfolio shows the path forward for an efficient HVAC future. Short-term technical implementation is achievable, reasonable, and effective given the proposed control algorithm in its current state, and with further research into occupancy prediction models and IAQ-productivity models significant additional gains and mitigations of negative side-effects is imminently achievable. By identifying barriers to adoption, developers of such a technology have a well mapped battlefield on which they will be attempting to win the attention and dollars of residential, commercial, and other users. Leveraging the effectiveness of the solution with an understanding of non-technical factors inhibiting adoption allows for near-term action on this pressing problem.
BS (Bachelor of Science)