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A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Journal Article · · Sensors
DOI:https://doi.org/10.3390/s20205947· OSTI ID:1770910
 [1]
  1. National Renewable Energy Lab. (NREL), Golden, CO (United States)
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Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.
Research Organization:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
Grant/Contract Number:
AC36-08GO28308
OSTI ID:
1770910
Report Number(s):
NREL/JA--5500-77488; MainId:27424; UUID:b129e4bf-a432-4a5a-9531-116c73646407; MainAdminID:19944
Journal Information:
Sensors, Journal Name: Sensors Journal Issue: 20 Vol. 20; ISSN 1424-8220
Publisher:
MDPI AGCopyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
building sensors
data imputation
ensemble method
machine learning
missing data
pattern recognition