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Open AccessArticle

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

1
School of Electrical Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
2
Department of Software, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1772; https://doi.org/10.3390/s20061772
Received: 24 December 2019 / Revised: 6 March 2020 / Accepted: 22 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Sensor Technologies for Smart Industry and Smart Infrastructure)
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. View Full-Text
Keywords: missing-value imputation; electric energy consumption data; smart meter; deep learning; multilayer perceptron; ensemble learning missing-value imputation; electric energy consumption data; smart meter; deep learning; multilayer perceptron; ensemble learning
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Jung, S.; Moon, J.; Park, S.; Rho, S.; Baik, S.W.; Hwang, E. Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation. Sensors 2020, 20, 1772.

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