Abstract
Nontechnical loss has been a significant factor to influence the profits of electric power companies. In the smart electricity consumption environment, the population of smart metering is helpful to collect the load data directly and promptly. A method of nontechnical loss detection, including offline parameter optimization and online detection, is proposed in this paper. With the updating of history data, the detection accuracy gets higher till the optimal point. Extreme learning machine is adopted to detect the nontechnical loss and genetic algorithm is used to find out the optimal parameters for each user. Finally, in the case study the real-time detection system assesses the classification accuracy and fault detection accuracy, verifying the stability of this method and advantages in reducing the cost.
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