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• W1978572130 abstract "Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features." @default.
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• W1978572130 date "2006-10-01" @default.
• W1978572130 modified "2023-09-27" @default.
• W1978572130 title "Customer Information System Data Pre-Processing with Feature Selection Techniques for Non-Technical Losses Prediction in an Electricity Market" @default.
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• W1978572130 doi "https://doi.org/10.1109/icpst.2006.321964" @default.
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