To reduce energy consumption, it is important to detect energy faults, which are defined as potential sources of wasted energy in building operations. However, it is very difficult even for HVAC specialists to grasp the whole structure of energy consumption because building energy management systems (BEMSs) data are multidimensional. To address this issue, we applied machine learning approaches to analyze nonspecific energy faults using operation data in a real office building. Based on the results in this paper, we propose a diagnostic method of detecting inefficient energy use having the characteristics of both experts’ empirical knowledge and a datadriven approach. We start with an existing if-then rule, compensate for the incompleteness of the knowledge with the information given by the data behavior, and attempt to extract hidden causal variables of inefficient energy use. In the proposed method, we employ three types of machine learning to widen the scope of analysis and secure adequate versatility while maintaining a high degree of explainability of the preliminary knowledge. The results show that this approach is potentially effective in energy faults analysis.
Citation: ASHRAE Transactions – Volume 121, Part 1, Chicago, IL
Product Details
- Published:
- 2015
- Number of Pages:
- 12
- File Size:
- 1 file , 5.1 MB
- Product Code(s):
- D-CH-15-017
