Abstract
In order to solve the problem of huge and messy data in the process of analyzing energy consumption structure in different regions, an energy consumption structure analysis method based on K-means clustering algorithm is proposed, and the elbow method and contour coefficient method are used to analyze the data in Qinghai Province. The consumption structure was analyzed and the algorithm was verified. The results show that the algorithm can efficiently and quickly perform data mining and clustering based on local economic and environmental characteristics, which greatly improve the convenience of energy consumption structure analysis.
Highlights
At present, countries all over the world have higher and higher requirements for their domestic energy transition, and the global energy transition process is accelerating
The purpose of K-Means clustering is to divide n points into k clusters, so that each point belongs to the cluster corresponding to its nearest mean, which is invoked as the cluster standard
K-Means is a clustering algorithm that finds k clusters of a given data set. It is called K-Means because it can find k different clusters, and the center of each cluster uses the value contained in the cluster The number k of clusters formed by the mean value calculation is specified by the user, and each cluster is described by its centroid, which is the center of all points in the cluster
Summary
Countries all over the world have higher and higher requirements for their domestic energy transition, and the global energy transition process is accelerating. Countries in the world are actively involved in promoting low-carbon energy under the common goal of the Paris Agreement. Using different analysis methods for different types of objects can save time greatly. Domestic and foreign scholars have proposed many improved algorithms based on the Kmeans clustering algorithm and applied to various occasions, such as MinMax K-means algorithm[1], Kmor algorithm[2] and Seeded-Kmeans algorithm[3]. This paper proposes a k-means clustering algorithmbased analytical method suitable for energy consumption structure in different regions. The K-means clustering algorithm combines local economic information and environmental information to quickly sort out several different objects, and select appropriate indicators to analyze the local energy consumption structure
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