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Method of estimation of missing data in AMI system
... Therefore, although this method may be effective in certain datasets that have cyclic patterns, it is not suitable for datasets that have multiple types of power consumption patterns. Experiments are conducted on compensation methods based on ARIMA and long short-term memory (LSTM) estimations, which are compensation methods based on time-series estimation, in addition to these two conventional methods [29]. In this research, we study a hybrid method that combines the advantages of the linear interpolation method and those of the LSTM estimation-based compensation method; subsequently, we perform a comparative analysis. ...
With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods.
Forecasting and modeling building energy profiles require tools able to discover patterns within large amounts of collected information. Clustering is the main technique used to partition data into groups based on internal and a priori unknown schemes inherent of the data. The adjustment and parameterization of the whole clustering task is complex and submitted to several uncertainties, being the similarity metric one of the first decisions to be made in order to establish how the distance between two independent vectors must be measured. The present paper checks the effect of similarity measures in the application of clustering for discovering representatives in cases where correlation is supposed to be an important factor to consider, e. g., time series. This is a necessary step for the optimized design and development of efficient clustering-based models, predictors and controllers of time-dependent processes, e. g., building energy consumption patterns. In addition, clustered-vector balance is proposed as a validation technique to compare clustering performances.
C Data quality of electricity consumption data in a smart grid environment. Renew. Sustain. Energy2017 Rev. 75 98-105. hen W.; Zhou K.; Yang S.; Wu C Data quality of electricity consumption data in a smart grid environment
- W Zhou
- K Yang
- S Wu
Master degree , School Korea university , Republic of Korea , 2019 . aehyun Koh, Outlier Detection and Imputation Method for Smart Meter Data Using Pattern analysis
- Koh Aehyun
Master degree , Dankook university , Republic of Korea , 2019 . in-Oh Kim, A Study on the Prediction of ShortTerm Electric Power Load by Deep Learning System
- In- Oh Kim
- Kim Oh