Missing Data estimation with a bi-dimensional adaptive weighted method for power grid data

Xi, Wei; Li, Mingwei; Zhou, Jianjia; Deng, Yan; Zhang, Qingqing

doi:10.1088/1757-899x/490/4/042025

Cited by 2 publications

(1 citation statement)

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“…Then, it calculates the distance between feature vectors, selects k most similar past situations according to the distances and estimates the missing data point. Wang et al [11] proposed a method of adopting a weighted summation of results from both linear regression (LR) and NN methods. The weight for yielding the outcome depends on the errors of the two models, which makes the imputation result almost similar to the output from the model with a lower error.…”

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%