Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-03, Vol.20 (6), p.1772
Main Authors: Jung, Seungwon, Moon, Jihoon, Park, Sungwoo, Rho, Seungmin, Baik, Sung Wook, Hwang, Eenjun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Bagging
Datasets
deep learning
electric energy consumption data
Electrical loads
Electricity consumption
Energy consumption
Energy management
Energy management systems
ensemble learning
Experiments
Forecasting
Machine learning
Malfunctions
Methods
Missing data
missing-value imputation
multilayer perceptron
Multilayer perceptrons
Neural networks
Signal transmission
smart meter
Variables
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Summary:For efficient and effective energy management, accurate energy consumption forecasting is required in energy management systems (EMSs). Recently, several artificial intelligence-based techniques have been proposed for accurate electric load forecasting; moreover, perfect energy consumption data are critical for the prediction. However, owing to diverse reasons, such as device malfunctions and signal transmission errors, missing data are frequently observed in the actual data. Previously, many imputation methods have been proposed to compensate for missing values; however, these methods have achieved limited success in imputing electric energy consumption data because the period of data missing is long and the dependency on historical data is high. In this study, we propose a novel missing-value imputation scheme for electricity consumption data. The proposed scheme uses a bagging ensemble of multilayer perceptrons (MLPs), called softmax ensemble network, wherein the ensemble weight of each MLP is determined by a softmax function. This ensemble network learns electric energy consumption data with explanatory variables and imputes missing values in this data. To evaluate the performance of our scheme, we performed diverse experiments on real electric energy consumption data and confirmed that the proposed scheme can deliver superior performance compared to other imputation methods.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20061772