Abstract
The distribution network is constantly being constructed and developed. So far, a large amount of data has been accumulated, covering residential users, industrial and commercial users, power distribution stations, electric meters, power quality and other data. However, power load data is a large amount of time series data, which has the characteristics of a large scale of users and a high density of data acquisition. It is difficult to query and analyze these data efficiently with the existing technology. To improve the efficiency of query and analysis, based on HBase and inverted index technology, this paper proposes a massive time series data index technology to improve the efficiency of query and analysis of massive time series data, and effectively support the power companies' mining and clustering of user behavior analysis. The experimental results show that the efficiency of this method is significantly higher than that of traditional sequential storage when the number of time series increases to a certain level.
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