Name
Abstract | ||
|---|---|---|
In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users’ preferences, resulting in poor estimates of the users’ preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user’s feedback, allowing it to adapt to the users’ individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2% to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model’s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.1145/3057730 | ACM TIST |
Keywords | Field | DocType |
User behaviour modeling and learning, agents, computational sustainability, machine learning | Air conditioning,Ventilation (architecture),Computer science,Computational sustainability,Artificial intelligence,Simulation,HVAC,Thermal comfort,Bayesian network,Energy consumption,Reliability engineering,Machine learning,ASHRAE 90.1 | Journal |
Volume | Issue | ISSN |
9 | 3 | 2157-6904 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Frederik Auffenberg | 1 | 1 | 1.02 |
| Stephen Snow | 2 | 25 | 10.33 |
| Sebastian Stein | 3 | 394 | 42.61 |
| alex rogers | 4 | 2500 | 183.76 |