Abstract
Fault detection and diagnosis play a crucial role in energy savings in heating, ventilation, and air conditioning systems. It enables timely and appropriate repairs to prevent system malfunctioning and reduce energy waste. In this paper, a proposed feature selection algorithm or a neurodynamic optimization algorithm based on information gain is used for selecting a certain number of significant features, and then a stacking classifier is utilized to classify data into several different fault types by using the selected features. Experimental results are elaborated to demonstrate the superior performance of the proposed method against baselines in terms of accuracy on most of the datasets. © 2023 IEEE
| Original language | English |
|---|---|
| Title of host publication | 2023 13th International Conference on Information Science and Technology (ICIST) |
| Publisher | IEEE |
| Pages | 432-440 |
| Number of pages | 9 |
| ISBN (Electronic) | 979-8-3503-1392-5 |
| ISBN (Print) | 979-8-3503-1393-2 |
| DOIs | |
| Publication status | Published - 2023 |
| Event | 13th International Conference on Information Science and Technology (ICIST 2023) - Texas A&M University & Renaissance Cairo Mirage City Hotel, Doha & Cairo, Egypt Duration: 8 Dec 2023 → 14 Dec 2023 https://conference.cs.cityu.edu.hk/icist/ |
Publication series
| Name | |
|---|---|
| ISSN (Print) | 2164-4357 |
| ISSN (Electronic) | 2573-3311 |
Conference
| Conference | 13th International Conference on Information Science and Technology (ICIST 2023) |
|---|---|
| Country/Territory | Egypt |
| City | Doha & Cairo |
| Period | 8/12/23 → 14/12/23 |
| Internet address |
Research Keywords
- Fault detection and diagnosis
- heating ventilation and air conditioning (HVAC) systems
- feature selection
- classification
