Based on the 2017 Hangzhou electricity consumption data of industrial enterprises, this paper aims to establish correlation networks to filter out the central enterprises and their connections in the city. First, we exclude the seasonal effect on electricity consumption and calculate the pure correlation of these enterprises by using the conditional Pearson correlation model. Next, we adjust the correlation coefficient thresholds by making the node degree distribution approximately satisfy the power-law distributions. Then, the empirical analysis on network properties is carried out, including the heavy-head distribution, the assortative mixing, and the small-world property. Based on these properties, we propose an approach for identifying central enterprises in the electricity consumption correlation network. The study shows that the central enterprises are closely related and can promote the connection between other non-central enterprises. Decision-makers can regulate the central enterprises detected by our approach in order to affect the overall development of the city.
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