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Towards missing electric power data imputation for energy management systems
Ming Chang Wang,
Chih Fong Tsai
, Wei Chao Lin
資訊管理學系
研究成果
:
雜誌貢獻
›
期刊論文
›
同行評審
42
引文 斯高帕斯（Scopus）
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Keyphrases
Energy Management System
100%
Electricity Data
100%
Data Imputation
100%
Machine Learning Techniques
80%
Peak Time
60%
Summer Season
60%
K-nearest
60%
Imputation Methods
60%
Statistical Methods
40%
Least Squares Support Vector Regression (LSSVR)
40%
Taiwan
20%
Data Mining Techniques
20%
Multilayer Perceptron
20%
Power Consumption
20%
Result-oriented
20%
Electric Current
20%
Voltage-current
20%
Linear Interpolation
20%
Effective Energy
20%
Single Feature
20%
Feature Value
20%
Autoregressive Integrated Moving Average (ARIMA)
20%
Missing Data
20%
High Error Rate
20%
Data Mining Applications
20%
Three-machine
20%
Demand for Electricity
20%
Linear Interpolation Model
20%
Mean Value Interpolation
20%
Missing Features
20%
Missing Data Imputation
20%
Statistical Learning Methods
20%
Engineering
Energy Management System
100%
Machine Learning Method
100%
Peak Time
75%
Summer Season
75%
Nearest Neighbor
75%
Linear Interpolation
50%
Support Vector Machine
50%
Experimental Result
25%
Electric Power Utilization
25%
Data-Mining Technique
25%
Moving Average
25%
Periodic Time
25%
Effective Energy
25%
Error Rate
25%
Interpolation Model
25%
Single Feature
25%
Perceptron
25%
Mathematics
Nearest Neighbor
100%
Imputation Method
100%
Data Imputation
100%
Support Vector Machine
66%
Linear Interpolation
66%
Statistical Method
66%
Error Rate
33%
Data Mining Technique
33%
Multilayer Perceptron
33%
Year Period
33%
Periodic Time
33%
Autoregressive Integrated Moving Average
33%
Feature Value
33%
Photoelectric Emission
33%
Chemical Engineering
Learning System
100%
Support Vector Machine
50%
Multilayer Neural Networks
25%