Prediction of residential water consumption based on K-means and improved KNN algorithm

Abstract

Accurate prediction of residential water consumption is the basis of optimal allocation of urban water resources and an important way to achieve high quality development of the country. In order to improve the accuracy of residential water prediction, aiming at the shortcomings of KNN (K-nearest Neighbor Algorithm) algorithm in water consumption prediction, a water consumption prediction method based on K-means and improved KNN algorithm is proposed. The K-means clustering method was used to determine the categories of historical samples of residential water consumption, and the way of searching similar historical sample sets in KNN algorithm was improved and optimized. The prediction model was built, and the prediction results were compared with those of SVM model and BP neural network model. The results show that the prediction accuracy of the prediction model based on K-means and the improved KNN algorithm is greatly improved, which is feasible and practical, and can be applied to the prediction of residential water consumption.