Bagging ensemble of multilayer perceptrons for missing electricity consumption data imputation

Seungwon Jung, Jihoon Moon, Sungwoo Park, Seungmin Rho, Sung Wook Baik, Eenjun Hwang

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number1772
JournalSensors (Switzerland)
Volume20
Issue number6
DOIs
Publication statusPublished - 2020 Mar 2

Keywords

  • Deep learning
  • Electric energy consumption data
  • Ensemble learning
  • Missing-value imputation
  • Multilayer perceptron
  • Smart meter

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering

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