A Hybrid Algorithm for Missing Data Imputation and Its Application to Electrical Data Loggers

Turrado, Concepción Crespo; Lasheras, Fernando Sánchez; Calvo-Rolle, José Luís; Piñón-Pazos, Andrés-José; Melero, M.G.; Juez, Francisco Javier de Cos

doi:10.3390/s16091467

Cited by 4 publications

(1 citation statement)

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“…According to the conditions, suitable models were selected among the small models and utilized to impute the missing data point. Turrado et al [15] presented a self-organized maps neural network-based method using several factors, such as each phase current, voltage from phase to phase, and voltage from phase to neutral. They constructed a data matrix using those factors and extracted director vectors using the self-organized maps neural network.…”

Section: Introductionmentioning

confidence: 99%

Bagging Ensemble of Multilayer Perceptrons for Missing Electricity Consumption Data Imputation

Jung

Moon

Park

et al. 2020

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%