A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data

Liu, Xue; Ding, Yong; Tang, Hao; Xiao, Feng

doi:10.1016/j.enbuild.2020.110601

Cited by 93 publications

(35 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Contrarily, the modelbased quantitative methods focus more on using a reference model to compare the measured data from the system [42,43,52]. Data-based quantitative methods use statistics for, among others, data clustering [53][54][55], pattern recognition [56][57][58] and classification [59][60][61] to extract the knowledge from the data.…”

Section: Methods Categorizations For Fddmentioning

confidence: 99%

“…CART was used in four articles on FD for the EST energy-use group. This method is used because it provides a decision tree with if-then rules, meaning that the outcome is interpretable by both humans and computers [55,61,123]. Regarding classification accuracy (CA) [61], it was between 80 and 90%, while the rest did not provide a detection-accuracy measure.…”

Section: Algorithm Distributionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

This review aims to provide an up-to-date, comprehensive, and systematic summary of fault detection and diagnosis (FDD) in building systems. The latter was performed through a defined systematic methodology with the final selection of 221 studies. This review provides insights into four topics: (1) glossary framework of the FDD processes; (2) a classification scheme using energy system terminologies as the starting point; (3) the data, code, and performance evaluation metrics used in the reviewed literature; and (4) future research outlooks. FDD is a known and well-developed field in the aerospace, energy, and automotive sector. Nevertheless, this study found that FDD for building systems is still at an early stage worldwide. This was evident through the ongoing development of algorithms for detecting and diagnosing faults in building systems and the inconsistent use of the terminologies and definitions. In addition, there was an apparent lack of data statements in the reviewed articles, which compromised the reproducibility, and thus the practical development in this field. Furthermore, as data drove the research activity, the found dataset repositories and open code are also presented in this review. Finally, all data and documentation presented in this review are open and available in a GitHub repository.

show abstract

Section: Methods Categorizations For Fddmentioning

confidence: 99%

Section: Algorithm Distributionmentioning

confidence: 99%

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

et al. 2022

View full text Add to dashboard Cite

show abstract

“…The DBSCAN can precisely identify noise points between different clusters, which respond well to the occurrence characteristics of abnormal monitoring data. 2 Given that the uplift pressure is closely related to the reservoir water level, and it presents similar one-to-one correspondence characteristics, the DBSCAN can identify classes with arbitrary shapes [37], so it can precisely identify the monitoring data clustering of the uplift pressure in the normal and the abnormally stable state. 3 For data with high-dimensional, uneven density and large distance, the clustering effect of the DBSCAN will be worse.…”

Section: Anomaly Identification Model Of Uplift Pressure Based On Dbscanmentioning

confidence: 99%

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

Zhao

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

As an essential load of the concrete dam, the abnormal change of uplift pressure directly threatens the safety and stability of the concrete dam. Therefore, it is of great significance to accurately and efficiently excavate the hidden information of the uplift pressure monitoring data to clarify the safety state of the concrete dam. Therefore, in this paper, density-based spatial clustering of applications with noise (DBSCAN) method is used to intelligently identify the abnormal occurrence point and abnormal stable stage in the monitoring data. Then, an application method of measured uplift pressure is put forward to accurately reflect the spatial distribution and abnormal position of uplift pressure in the dam foundation. It is easy to calculate and connect with the finite element method through self-written software. Finally, the measured uplift pressure is applied to the finite element model of the concrete dam. By comparing the structural behavior of the concrete dam under the design and measured uplift pressure, the influence of abnormal uplift pressure on the safety state of the concrete dam is clarified, which can guide the project operation. Taking a 98.5 m concrete arch dam in western China as an example, the above analysis ideas and calculation methods have been verified. The abnormal identification method and uplift pressure applying method can provide ideas and tools for the structural diagnosis of a concrete dam.

show abstract

“…With the rapid development of the construction industry, ecological problems and energy crisis are becoming increasingly severe. The construction sectors account for nearly 40% of global energy consumption [1,2] and produce more than 30% of carbon dioxide emissions [2] as well as more than 10 billion tons of construction waste per year [3]. Among them, environmental pollution and energy consumption problems caused by a hospital building should not be underestimated.…”

Section: Introductionmentioning

confidence: 99%

BIM-Based Green Hospital Building Performance Pre-Evaluation: A Case Study

Zhan

et al. 2022

Sustainability

View full text Add to dashboard Cite

With ecological problems and energy crises intensifying today, greening is essential to sustainable development. Compared with other types of buildings, hospital buildings account for a relatively larger proportion of building energy consumption. In order to realize the rapid cycle optimization of a green hospital project in the design stage and improve the green grade of the building, a pre-evaluation Building Information Model (BIM) of green hospital building performance was established in this study. Firstly, the literature review and expert consultation established the building performance pre-evaluation index system for green hospitals. Then, BIM technology is taken to extract data needed for building a performance pre-evaluation system, and the Cloud Model and the Matter–Element Extension Theory are used to build models. The final green grade calculation is realized in MATLAB. Finally, the Maluan Bay Hospital is taken as an example to test the applicability and effectiveness of the proposed model. The results show that the green hospital building performance pre-evaluation model has advantages of simulation, cyclic optimization and fuzzy quantification, which can effectively guide the design and construction of a green hospital.

show abstract

A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data

Cited by 93 publications

References 42 publications

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review

Intelligent Anomaly Identification of Uplift Pressure Monitoring Data and Structural Diagnosis of Concrete Dam

BIM-Based Green Hospital Building Performance Pre-Evaluation: A Case Study

Contact Info

Product

Resources

About