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A Comparison of Methods for Missing Data Treatment in Building Sensor Data

Lookup NU author(s): Dr Mehdi Pazhoohesh, Dr Zoya PourmirzaORCiD, Professor Sara Walker



This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by IEEE, 2019.

For re-use rights please refer to the publisher's terms and conditions.


Data collection is a fundamental component in the study of energy and buildings. Errors and inconsistencies in the data collected from test environment can negatively influence the energy consumption modelling of a building and other control and management applications. This paper addresses the gap in the current study of missing data treatment. It presents a comparative study of eight methods for imputing missing values in building sensor data. The data set used in this study, are real data collected from our test bed, which is a living lab in the Newcastle University. When the data imputation process is completed, we used Mean Absolute Error, and Root Mean Squared Error methods to evaluate the difference between the imputed values and real values. In order to achieve more accurate and robust results, this process has been repeated 1000, and the average of 1000 simulation is demonstrated in this paper. Finally, it is concluded that it is necessary to identify the percentage of missing data before selecting the proper imputation method, in order to achieve the best and building data, data imputation; missing value; KNN; MCMC; MAE; RMSE

Publication metadata

Author(s): Pazhoohesh M, Pourmirza Z, Walker S

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE)

Year of Conference: 2019

Pages: 255-259

Online publication date: 07/10/2019

Acceptance date: 01/09/2019

Date deposited: 06/03/2020

ISSN: 2575-2693

Publisher: IEEE


DOI: 10.1109/SEGE.2019.8859963