Regional integrated energy side data clustering method based on K-means

Inhalt:
Conventional regional comprehensive energy side data clustering method has the problem of imperfect similarity measurement model, which leads to poor clustering quality. A regional comprehensive energy side data clustering method based on K-means is designed. The coupling relation between various energy sources was clarified, the sample data of regional comprehensive energy was obtained, the nearest neighbor density value of the sample was calculated, the similarity measurement model was constructed by K-means, the data after unified dimension was cut and reassembled, and the energy side data clustering method was designed. Experimental results: The clustering quality mean values of the regional comprehensive energy side data clustering method in this paper and the other two regional comprehensive energy side data clustering methods are 0.675, 0.491 and 0.518 respectively, indicating that the clustering performance is improved after the fusion of k-means algorithm and regional comprehensive energy side data clustering method.