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Développement d’algorithmes d’autocorrection pour les thermostats en utilisant les capacités d’API ouvertes.

Development of self-correction algorithms for thermostats using openAPI capabilities.

Numéro : 3449

Auteurs : CHEN Y., CROWE E., GRANDERSON J.

Résumé

Advanced HVAC data analytics tools such as fault detection & diagnostics (FDD) are growing in popularity in large commercial buildings, and recent research has demonstrated potential for such tools to automatically correct certain faults. However, market adoption of such technologies is very low in small- and medium-sized buildings (SMBs). Furthermore, a lack of on-site maintenance teams and limited maintenance expenses restrict rapid, effective resolution after faults occur and are identified in HVAC systems. Those factors could cause significant energy waste and downgrading system performance (e.g., decreased occupant comfort and reduced system lifetime). Considering commercial buildings under 50k square feet comprise 94% of all commercial buildings in the U.S, there is a significant need to develop cost-effective solutions for those buildings.
While much attention has been given to the benefits of basic smart thermostats, more prevalent mid-tier connected thermostats provide significant untapped opportunities to advance the state of operational practice in SMBs. These devices now offer two-way APIs that can be combined with everyday computing resources to open the door to continuous, automated monitoring and correction of the most common problems in HVAC control settings. Such technology presents the potential for a lightweight solution to address monitoring and control barriers.
In this paper, we present the results of a study to develop self-correction algorithms to correct common setting faults in SMBs, including inefficient HVAC setpoints, and wrong schedule setting when using thermostats. The detection and self-correction actions were achieved by employing two-way APIs embedded in thermostat products. The developed correction algorithms were evaluated in a lab environment. The results show that common thermostat setting faults can be efficiently corrected. Consequently, energy waste that commonly goes undiscovered, can be effectively avoided. We conclude with a discussion of how these solutions can be delivered to market by OEMs or as managed service offerings from thermostat installers and other service providers.

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Pages : 12 p.

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Détails

  • Titre original : Development of self-correction algorithms for thermostats using openAPI capabilities.
  • Identifiant de la fiche : 30032931
  • Langues : Anglais
  • Source : 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 15/07/2024

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