GUANSi-yuan,ZHANG Qiao-zhen.Water Demand Prediction Utilizing Filtering Reconstruction Time Series Regression Algorithm[J].China Water & Wastewater,2025,41(7):63-68.
Water Demand Prediction Utilizing Filtering Reconstruction Time Series Regression Algorithm
China Water & Wastewater[ISSN:1000-4062/CN:12-1073/TU]
volume:
第41卷
Number:
第7期
Page:
63-68
Column:
Date of publication:
2025-04-01
- Keywords:
- water demand prediction; Savitzky-Golay filter; correlation analysis; filtering reconstruction; time series
- Abstract:
- Accurate prediction of user water demand is essential for water treatment plants to rationally allocate water supply, improve water supply efficiency, and ensure water supply safety. The auto-regressive model, reconstructed through feature-based filtering, effectively eliminates anomalous data while preserving trend information. Through correlation analysis, it was determined that there existed a significant linear relationship between urban water demand and time. By employing a sliding window approach to analyze the time series of the original data in conjunction with filtering reconstruction, the mean absolute percentage error was reduced to 2.26%, with approximately 92% of the data points exhibiting an error range within 5%. This performance is notably superior to that achieved through single time series analysis and other machine learning methods. The water demand prediction model, developed using the specified algorithm, was implemented in the production scheduling of a water company. As a result, the power consumptions associated with product water pressurization and water supply pressurization were reduced by 5.64% and 4.55%, respectively.
Last Update:
2025-04-01