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

Abstract

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 result.