A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery From Building Operational Data
The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispen...
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| Published in | Frontiers in energy research Vol. 9 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
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Frontiers Media S.A
29.03.2021
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field. |
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| AbstractList | The rapid development in data science and the increasing availability of building operational data have provided great opportunities for developing data-driven solutions for intelligent building energy management. Data preprocessing serves as the foundation for valid data analyses. It is an indispensable step in building operational data analysis considering the intrinsic complexity of building operations and deficiencies in data quality. Data preprocessing refers to a set of techniques for enhancing the quality of the raw data, such as outlier removal and missing value imputation. This article serves as a comprehensive review of data preprocessing techniques for analysing massive building operational data. A wide variety of data preprocessing techniques are summarised in terms of their applications in missing value imputation, outlier detection, data reduction, data scaling, data transformation, and data partitioning. In addition, three state-of-the-art data science techniques are proposed to tackle practical data challenges in the building field, i.e., data augmentation, transfer learning, and semi-supervised learning. In-depth discussions have been presented to describe the pros and cons of existing preprocessing methods, possible directions for future research and potential applications in smart building energy management. The research outcomes are helpful for the development of data-driven research in the building field. |
| Author | Fan, Cheng Wang, Xinghua Chen, Meiling Wang, Jiayuan Huang, Bufu |
| Author_xml | – sequence: 1 givenname: Cheng surname: Fan fullname: Fan, Cheng – sequence: 2 givenname: Meiling surname: Chen fullname: Chen, Meiling – sequence: 3 givenname: Xinghua surname: Wang fullname: Wang, Xinghua – sequence: 4 givenname: Jiayuan surname: Wang fullname: Wang, Jiayuan – sequence: 5 givenname: Bufu surname: Huang fullname: Huang, Bufu |
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