Speeding up processing data from millions of smart meters
Proceedings of the 5th ACM/SPEC international conference on Performance engineering
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According to the requirement of building data centers in State Grid project planning, the process of data cleaning was divided into two sub-processes in the data extraction process, namely, the abnormal values were set to NULL after detecting the electric quantity data, then, those data were predicted based on other valid values. To further improve the quality of data, we proposed a method which based on genetic neural network to handle the missing values. This method fully used the global search ability of genetic algorithm and the nonlinear mapping ability of neural network, so that the prediction accuracy of the data was greatly improved. The experiment shows that this method is feasible and effective in improving the prediction precision of data.