An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining

Xu, Yizhe; Yan, Chengchu; Shi, Jingfeng; Lu, Zefeng; Niu, Xiaofeng; Jiang, Yanlong; Zhu, Faxing

doi:10.1016/j.seta.2021.101092

Cited by 20 publications

(16 citation statements)

References 28 publications

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“…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%

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

et al. 2022

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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.

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Section: Methods Categorizations For Fddmentioning

confidence: 99%

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

et al. 2022

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“…The outliers were identified by setting upper and lower bounds. [38] According to the design data and operation experience of the reference coal power plant, seven variables were selected to judge if there were outliers. The upper and lower bounds of these variables were determined while the load of the plant ranged from 40% to 110% of the rated value, as listed in Table 3.…”

Section: Data Processingmentioning

confidence: 99%

Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

et al. 2021

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An artificial intelligence approach using machine learning (ML) is applied to predict and optimize the operational performance of the air‐cooled condenser (ACC) in a large‐scale power plant. The in situ data of one whole year are collected from a typical coal‐fired power plant equipped with an ACC. The ML models for predicting the gross power output and the ACC power consumption are established using the artificial neural network (ANN) and random forest (RF) algorithms, and appropriate parameters are determined for the ML models. As the RF models with lower errors are demonstrated to be superior to the ANN models, the RF models are selected. Then the RF models are simplified by identifying the primary input parameters and exploiting fewer input parameters. Based on the derived RF models, the operational performance of the ACC is examined when the total rotational frequency of air fans varies, and the maximum net power output is obtained with the optimal total rotational frequency of fans. Due to the optimization, the average net power output of the plant can be promoted by 0.9774 MW, and the mean of the total rotational frequency of fans increases by 11.40 Hz. Therefore, the proposed approach is beneficial and advantageous.

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“…Here, the building sector has become an area of concern in terms of minimizing the influence of massive energy consumption on environmental aspects [5]. The energy consumption of heating, ventilation and air-conditioning (HVAC) systems represents a considerable fraction of building energy consumption, accounting for nearly 50% of the total [6], and it still presents a continuous growth trend. IEA predicts that by 2050, global air-conditioning ownership will soar to two-thirds of all households [7], also resulting in an increase in global residential cooling demand of between 320% and 2270%, with the coupled effects of increased usage and longer cooling seasons [8].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

et al. 2022

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Accurate prediction of air-conditioning energy consumption in buildings is of great help in reducing building energy consumption. Nowadays, most research efforts on predictive models are based on large samples, while short-term prediction with one-month or less-than-one-month training sets receives less attention due to data uncertainty and unavailability for application in practice. This paper takes a government office building in Ningbo as a case study. The hourly HVAC system energy consumption is obtained through the Ningbo Building Energy Consumption Monitoring Platform, and the meteorological data are obtained from the meteorological station of Ningbo city. This study utilizes a Gaussian process regression with the help of a 12 × 12 grid search and prediction processing to predict short-term hourly building HVAC system energy consumption by using meteorological variables and short-term building HVAC energy consumption data. The accuracy R2 of the optimal Gaussian process regression model obtained is 0.9917 and 0.9863, and the CV-RMSE is 0.1035 and 0.1278, respectively, for model testing and short-term HVAC system energy consumption prediction. For short-term HVAC system energy consumption, the NMBE is 0.0575, which is more accurate than the standard of ASHRAE, indicating that it can be applied in practical energy predictions.

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An anomaly detection and dynamic energy performance evaluation method for HVAC systems based on data mining

Cited by 20 publications

References 28 publications

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

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

Performance Prediction and Optimization of the Air‐Cooled Condenser in a Large‐Scale Power Plant Using Machine Learning

Prediction of Hourly Air-Conditioning Energy Consumption in Office Buildings Based on Gaussian Process Regression

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