Bitcoin Mining Recognition Based on Community Detection with Electricity Consumption Data

Abstract

With the aim of bitcoin mining recognition, a method based on community detection is built with small sample of Sichuan daily bitcoin mining electricity consumption data. The method precisely categorizes Sichuan bitcoin mining entities according to their electricity consumption patterns. It is found that Sichuan bitcoin mining activity is strongly featured with seasonality and highly correlated with local hydropower generation. Such a discovery is a key not only to explaining the characteristics of the electricity consumption curves, but also to extending the method summarized from Sichuan case to other provinces. In practice, from Xinjiang power-intensive consumers, suspicious bitcoin miners are detected with the method and their mining activity is found to be correlated with local wind power generation. It is inferred that difficulty in green power utilization during the abundant period results in low electricity price which attracts bitcoin mining activity. By comparing the electricity consumption curves of Sichuan and Xinjiang (suspicious) bitcoin mining entities, a character of peak-valley complementation in temporal distribution implies a nomad electricity consumption of the miners, which could be influential to green power utilization as well as power transmission and distribution.