scholarly journals Missing Data Imputation in Internet of Things Gateways

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 425
Author(s):  
Cinthya M. França ◽  
Rodrigo S. Couto ◽  
Pedro B. Velloso
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Internet Of Things ◽  
Execution Time ◽  
Regression Models ◽  
Weather Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
The Neural Network

In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

scholarly journals Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

2019 ◽  
Vol 9 (1) ◽  
pp. 204 ◽  
Author(s):  
Taeyoung Kim ◽  
Woong Ko ◽  
Jinho Kim
Keyword(s):  
Power Generation ◽  
Missing Data ◽  
Missing Values ◽  
Prediction Performance ◽  
Weather Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
Pv Generation ◽  
Pv Power Generation

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.

A Hybrid Approach to Missing Data

2010 ◽  
pp. 45-70
Author(s):  
Tshilidzi Marwala
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Missing Data ◽  
Hybrid Approach ◽  
Principal Component ◽  
Missing Data Imputation ◽  
The Neural Network ◽  
Genetic Algorithm Approach ◽  
Successful Approach

The problem of missing data in databases has recently been dealt with through the use computational intelligence. The hybrid of auto-associative neural networks and genetic algorithms has proven to be a successful approach to missing data imputation. Similarly, two auto-associative neural networks are developed to be used in conjunction with genetic algorithm to estimate missing data, and these approaches are compared to a Bayesian auto-associative neural network and genetic algorithm approach. One technique combines three neural networks to form a hybrid auto-associative network, while the other merges principal component analysis and neural networks. The hybrid of the neural network and genetic algorithm approach proves to be the most accurate when estimating one missing value, while a hybrid of principal component and neural networks is more consistent and captures patterns in the data more efficiently.

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