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• W4311143191 endingPage "105688" @default.
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• W4311143191 abstract "The existence of outliers and noise is one of the major causes of poor quality of the building performance data. Methods to address such abnormal data have received significant attention from several research interests including data-driven fault detection, diagnosis, and estimation (FDD&E), performance optimization, predictive modeling, pattern recognition of building power consumption, etc. However, there are many factors that may eventually lead to unreliable FDD&E results or data cleaning effects. For instance, the misconception of the statistical definition of outliers, the confusion about the concepts of outliers and noise, and the neglect of the potential impact of the outlier processing way on the study results, etc. This study gives an interpretation to the statistical definition of outliers using the domain knowledge in building energy systems. The outliers in the building performance data are classified into three categories. Based on such classification, an outlier management framework is developed to achieve reliable outlier detection results and accurate outlier estimates. In Case study 1, under the proposed outlier management framework, about 81.2%–88.6% of the power consumption observations can be identified as outliers or normal observations. In terms of the outlier estimation accuracy, the RMSE of the chiller/chilled water pumps/cooling water pumps power outlier estimation results can be limited to less than 5.2, 0.5 and 0.6 kWh. The results of Case study 1 show that the proposed outlier management framework is generic. The framework can help reducing unnecessary monitoring cost of the HVAC system. In Case study 2, proper outlier management method helps improve the chiller power prediction results. The RMSE of the Basic group, Method 1 group and Method 2 group power prediction results are 0.55, 0.54 and 0.2 kWh. The results of Case study 2 show that the proposed outlier management framework can contribute to improve the results reliability of building performance analysis related studies. In this study, an outlier management framework is developed for the building performance data. The proposed framework aims to improve the quality of the data cleaning work. In the following research, we would dedicate to improve the efficiency of the data cleaning work. In other word, to develop specific online outlier management algorithms for specific studies under the proposed framework." @default.
• W4311143191 created "2022-12-23" @default.
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• W4311143191 date "2023-04-01" @default.
• W4311143191 modified "2023-09-27" @default.
• W4311143191 title "An outlier management framework for building performance data and its application to the power consumption data of building energy systems in non-residential buildings" @default.
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• W4311143191 doi "https://doi.org/10.1016/j.jobe.2022.105688" @default.
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