Name
Title | ||
|---|---|---|
Sampling Using Fuzzy and Crisp Clustering to Improve Recall of Building Comfort Feedback |
Abstract | ||
|---|---|---|
The primary objective of a building energy management system is to ensure the comfort of its occupants. This paper reports experiments with a user feedback system for a central temperature controlled building. Individual room temperatures are further adjusted based on occupant feedback. The number of feedback points are limited creating a data imbalance. The paper describes how crisp and fuzzy clustering can be used to sample the data points when there is no feedback from the users. The results of the proposed sampling is compared with other sampling strategies. The goal of the research is to maximize the recall of feedback through machine learning. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/FUZZ-IEEE.2019.8858938 | 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) |
Keywords | Field | DocType |
building energy management system,user feedback system,central temperature controlled building,data imbalance,fuzzy clustering,data points,sampling strategies,crisp clustering,room temperatures,building comfort feedback recall,machine learning | Data point,Fuzzy clustering,Building management system,Computer science,Euclidean distance,Fuzzy logic,Artificial intelligence,Sampling (statistics),Cluster analysis,Recall,Machine learning | Conference |
ISSN | ISBN | Citations |
1544-5615 | 978-1-5386-1729-8 | 1 |
PageRank | References | Authors |
0.38 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ross MacDonald | 1 | 1 | 1.06 |
| Nikita Neveditsin | 2 | 1 | 0.72 |
| Pawan Lingras | 3 | 1408 | 143.21 |
| Zheng Qin | 4 | 1 | 0.38 |
| Trent Hillard | 5 | 1 | 0.38 |