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

RuoYuan Zhang; XingHang Wang

doi:10.1117/12.2640693

3 October 2022 Prediction of residential water consumption based on K-means and improved KNN algorithm

RuoYuan Zhang, XingHang Wang

Proceedings Volume 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022); 122900S (2022) https://doi.org/10.1117/12.2640693
Event: International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 2022, Zhuhai, China

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.

Citation Download Citation

RuoYuan Zhang and XingHang Wang "Prediction of residential water consumption based on K-means and improved KNN algorithm", Proc. SPIE 12290, International Conference on Computer Network Security and Software Engineering (CNSSE 2022), 122900S (3 October 2022); https://doi.org/10.1117/12.2640693

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available