A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data
The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quali...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
He, Ruikai [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives - Plonowska, Karolina A. ELSEVIER, 2018, an international journal of research applied to energy efficiency in the built environment, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:273 ; year:2022 ; day:15 ; month:10 ; pages:0 |
| Links: |
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| DOI / URN: |
10.1016/j.enbuild.2022.112372 |
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| 520 | |a The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. | ||
| 520 | |a The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. | ||
| 650 | 7 | |a Big engineering data |2 Elsevier | |
| 650 | 7 | |a Building energy management |2 Elsevier | |
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| 700 | 1 | |a Xiao, Tong |4 oth | |
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| 700 | 1 | |a Gu, Jiefan |4 oth | |
| 700 | 1 | |a Wei, Minchen |4 oth | |
| 700 | 1 | |a Xu, Peng |4 oth | |
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10.1016/j.enbuild.2022.112372 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001901.pica (DE-627)ELV058932461 (ELSEVIER)S0378-7788(22)00543-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl He, Ruikai verfasserin aut A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. Big engineering data Elsevier Building energy management Elsevier Data pre-processing Elsevier Xiao, Tong oth Qiu, Shunian oth Gu, Jiefan oth Wei, Minchen oth Xu, Peng oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:273 year:2022 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2022.112372 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 273 2022 15 1015 0 |
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10.1016/j.enbuild.2022.112372 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001901.pica (DE-627)ELV058932461 (ELSEVIER)S0378-7788(22)00543-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl He, Ruikai verfasserin aut A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. Big engineering data Elsevier Building energy management Elsevier Data pre-processing Elsevier Xiao, Tong oth Qiu, Shunian oth Gu, Jiefan oth Wei, Minchen oth Xu, Peng oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:273 year:2022 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2022.112372 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 273 2022 15 1015 0 |
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10.1016/j.enbuild.2022.112372 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001901.pica (DE-627)ELV058932461 (ELSEVIER)S0378-7788(22)00543-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl He, Ruikai verfasserin aut A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. Big engineering data Elsevier Building energy management Elsevier Data pre-processing Elsevier Xiao, Tong oth Qiu, Shunian oth Gu, Jiefan oth Wei, Minchen oth Xu, Peng oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:273 year:2022 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2022.112372 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 273 2022 15 1015 0 |
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10.1016/j.enbuild.2022.112372 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001901.pica (DE-627)ELV058932461 (ELSEVIER)S0378-7788(22)00543-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl He, Ruikai verfasserin aut A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. Big engineering data Elsevier Building energy management Elsevier Data pre-processing Elsevier Xiao, Tong oth Qiu, Shunian oth Gu, Jiefan oth Wei, Minchen oth Xu, Peng oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:273 year:2022 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2022.112372 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 273 2022 15 1015 0 |
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10.1016/j.enbuild.2022.112372 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001901.pica (DE-627)ELV058932461 (ELSEVIER)S0378-7788(22)00543-6 DE-627 ger DE-627 rakwb eng 610 VZ 44.94 bkl He, Ruikai verfasserin aut A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data 2022transfer abstract nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. Big engineering data Elsevier Building energy management Elsevier Data pre-processing Elsevier Xiao, Tong oth Qiu, Shunian oth Gu, Jiefan oth Wei, Minchen oth Xu, Peng oth Enthalten in Elsevier Science Plonowska, Karolina A. ELSEVIER Advanced head and neck surgical techniques: A survey of US otolaryngology resident perspectives 2018 an international journal of research applied to energy efficiency in the built environment Amsterdam [u.a.] (DE-627)ELV001764748 volume:273 year:2022 day:15 month:10 pages:0 https://doi.org/10.1016/j.enbuild.2022.112372 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.94 Hals-Nasen-Ohrenheilkunde VZ AR 273 2022 15 1015 0 |
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a rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data |
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A rule-based data preprocessing framework for chiller rooms inspired by the analysis of engineering big data |
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The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. |
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The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. |
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The rapid development of building energy consumption monitoring platforms makes engineering data more diverse, which facilitates the goal of reducing emissions. It is increasingly acknowledged that data preprocessing deserves the same attention as intelligent algorithms. In this work, the data quality issue of the engineering big data from non-demonstration complexes in China are analyzed thoroughly, and the analysis is based on clustering-based algorithms. We can conclude that the data of the hourly power of equipment groups are quality and stable, which is suitable for the benchmark to check other data. The quality of the data about pipes is acceptable. The number of data types about cooling towers is less, and the quality is worse. Regarding other data, the quality is unstable, so researchers should deal with those case-by-case. According to the above analysis, we proposed a convenient, rule-based data preprocessing framework that utilizes the law of physics, ensuring the strong coupling of multi-variants. After the data preprocessing, these engineering data are more reliable and can be used to improve performance or train models. Additionally, the proposed framework is more suitable for preprocessing multi-variant engineering data. |
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