Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

Energies

卷:10

Minkyung Kim¹  Sangdon Park¹  Jun Kyun Choi¹  Joohyung Lee²  Yongjae Joo³ 

[1] Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea;

[2] Department of Software, Gachon University, Seongnam 13120, Korea;

[3] Korea Electric Power Research Institute, Daejeon 305-760, Korea;

关键词: missing data;    power data;    imputation;    kNN algorithm;    learning;    smart meter;    energy system;   

DOI  :  10.3390/en10101668

来源: DOAJ

【 摘 要 】

This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

【 授权许可】

Unknown