A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining
The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has ma...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-09-01
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| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/13/9/2303 |
| _version_ | 1827726849292107776 |
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| author | Xiaodong Liu Shuming Zhang Weiwen Cui Hong Zhang Rui Wu Jie Huang Zhixin Li Xiaohan Wang Jianing Wu Junqi Yang |
| author_facet | Xiaodong Liu Shuming Zhang Weiwen Cui Hong Zhang Rui Wu Jie Huang Zhixin Li Xiaohan Wang Jianing Wu Junqi Yang |
| author_sort | Xiaodong Liu |
| collection | DOAJ |
| description | The purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors. |
| first_indexed | 2024-03-10T22:58:41Z |
| format | Article |
| id | doaj.art-c17854886611497b8fde22c3b0e6d57b |
| institution | Directory Open Access Journal |
| issn | 2075-5309 |
| language | English |
| last_indexed | 2024-03-10T22:58:41Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj.art-c17854886611497b8fde22c3b0e6d57b2023-11-19T09:52:11ZengMDPI AGBuildings2075-53092023-09-01139230310.3390/buildings13092303A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data MiningXiaodong Liu0Shuming Zhang1Weiwen Cui2Hong Zhang3Rui Wu4Jie Huang5Zhixin Li6Xiaohan Wang7Jianing Wu8Junqi Yang9School of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaSchool of Architecture, Tsinghua University, Beijing 100084, ChinaThe purpose of this study is to develop a framework to understand building energy usage pattern finding using data mining algorithms. Developing advanced techniques and requirements for carbon emission reduction provides higher demands for building energy efficiency. Research conducted so far has mainly focused on total energy consumption data clusters instead of time-series curve peculiarity. This research adopts the time-series cluster algorithm k-shape and the ARM Apriori method to study the simulation database generated by the official restaurant energy model. These advanced data mining techniques can discover potential information hidden in a big database that has not been identified by people. The results show that the restaurant time-series energy consumption curve can be clustered into four type patterns: Invert U, M, Invert V, and Multiple M. Each mode has its own variation characteristics. Two aspects for the solution of intensity and peak shift are proposed, achieving energy savings and focusing on different curve modes. The conclusion shows that the combination of time-series clustering and the ARM algorithm work flow can successfully discover the building operation pattern. Some solutions focusing on restaurant energy usage issues have been proposed, and future investigations should pay more attention to building area-influenced factors.https://www.mdpi.com/2075-5309/13/9/2303cluster analysisassociation rule mined analysisenergy consumptiontime-seriesdata mining |
| spellingShingle | Xiaodong Liu Shuming Zhang Weiwen Cui Hong Zhang Rui Wu Jie Huang Zhixin Li Xiaohan Wang Jianing Wu Junqi Yang A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining Buildings cluster analysis association rule mined analysis energy consumption time-series data mining |
| title | A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining |
| title_full | A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining |
| title_fullStr | A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining |
| title_full_unstemmed | A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining |
| title_short | A Workflow Investigating the Information behind the Time-Series Energy Consumption Condition via Data Mining |
| title_sort | workflow investigating the information behind the time series energy consumption condition via data mining |
| topic | cluster analysis association rule mined analysis energy consumption time-series data mining |
| url | https://www.mdpi.com/2075-5309/13/9/2303 |
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