## 1 Introduction
[PERSON] and [PERSON] (2019) addressed that missing values can be filled in using the global constant, moving average, or inference-based models. In their paper, missing values were handled using a simple moving average method with a window size of five samples. [PERSON] et al. (2019) proposed an imputation scheme using a recurrent neural network model to provide missing values in time series energy consumption data. The missing-value imputation scheme has been shown to obtain higher accuracies than those obtained using a multilayer perceptron model. [PERSON] et al. (2019) presented a novel and computationally efficient data processing method, called the optimally weighted average data imputation method, for imputing bad and missing load power measurements to create full power consumption data sets. The imputed data periods have a continuous profile with respect to the adjacent available measurements, which is a highly desirable feature for time-series power flow analyses. [PERSON] et al. (2019) proposed a methodology called the hybrid Long Short-Term Memory model with Bi-directional Imputation and Transfer Learning (LSTM-BIT). It integrates the powerful modeling ability of deep learning networks and flexible transferability of transfer learning. A case study on the electric consumption data of a campus lab building was utilized to test the method. To summarize the above research, researchers introduced, developed, and applied a specific imputation method applied for specific sensor types and missing data scenarios. However, normally in one building system, there are various types of sensors, including a thermometer, humidity sensor, flow meter, energy meter, differential pressure sensors, etc. There are also multiple missing data mechanisms (malfunction, power outage, local interferences, wireless radio transmissions error) and multiple randomness for missing data (missing not at random, missing at random, missing completely at random) for various sensors. The single imputation method may not be able to capture various missing data scenarios from various sensors. Few researchers study the compatibility of a single technique on various sensors with missing data. A few papers are found to work on the selection and use of multiple data imputation methods and they are introduced in the following paragraph. [PERSON] et al. [14] explored the use of missing data imputation and clustering on building electricity consumption data. The objective was to compare two data imputation methods: Amelia multiple imputation and cubic spline imputation. The results of this study suggest that using multiple imputation to fill in missing data prior to performing clustering analysis results in more informative clusters. [PERSON] et al. [15] focused on improving and correcting the monitored data set. They tried to identify the reasons for data losses and then look for recovery options. If lengths of periods of missing data are not exceeding the predefined thresholds, these periods are interpolated. Interpolation thresholds are set depending on the frequency of sensor monitoring and dynamic behavior. Continuous data are interpolated using linear and polynomial interpolation. [PERSON] et al. [16] filled the missing values with regression and linear interpolation for short and long periods. They also addressed that (a) it is necessary to detect the missing gaps between the data acquired as it is also an important factor to indicate the reliability of the data, (b) these missing gaps are always undeniable and lowering the amount of meaningful calculations, (c) it is crucial to make the missing gaps fewer as data with fewer gaps is considered good quality data, and (d) there are different methods available for handling the missing values, e.g., regression, depending on the nature of the data and other parameters, such as computations, precision, robustness, and accuracy. [PERSON] et al.