Abstract
Today's buildings are not only energy intensive, but also information intensive. Massive amounts of operational data are available for knowledge discovery. Data mining (DM) has excellent ability in extracting insights from massive data. This paper performs a case study on the assessment of building operational performance using DM techniques. Typical DM techniques are compared and considerations for choosing specific DM techniques for the case study are presented. The methodology developed has been applied to analyze the data retrieved from a university building in Hong Kong. Useful insights have been obtained to identify typical operation patterns and energy conservation opportunities.
| Original language | English |
|---|---|
| Pages (from-to) | 1070-1078 |
| Number of pages | 9 |
| Journal | Energy Procedia |
| Volume | 111 |
| DOIs | |
| Publication status | Published - 1 Mar 2017 |
| Event | 8th International Conference on Sustainability in Energy and Buildings, SEB 2016 - Turin, Italy Duration: 11 Sept 2016 → 13 Sept 2016 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Big Data
- Building Automation System
- Building Energy Conservation
- Data Mining
- Intelligent Building
ASJC Scopus subject areas
- General Energy
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