A hybrid model based on stacking and multi-correction mechanisms for urban water demand prediction⁎This work is supported by Key projects from Ministry of Sci-ence and Technology (No.2017ZX07207005-01), National Natural Science Foundation of China (No.61533013, 61633019), Shaanxi Provincial Key Project (2018ZDXMGY-168) and Shanghai Project (17DZ1202704). Issue 2 (2020)

• Record Type: Journal Article
• Authors: Lan, Yang; Wang, Jingcheng; Bai, Miaoshun; Brahmia, Ibrahim; Xu, Haotian; Hu, Piao; Long, Yuhao; Zhang, Yeming
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 16685
• Page End: 16690
• Publication Date: 2020
• Subjects: Water supply system -- Prediction methods -- Hybrid models -- Stacking methods -- Multi-correction mechanisms -- Neural-network models -- Time-series analysis; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1100
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Water demand prediction is the key link for the effective operation of urban intelligent water supply system. Since the non-linearity and complex variability of water consumption, it is difficult for traditional water demand prediction models to guarantee high accuracy for a long period. Different holiday types and even tiny changes in temperatures can affect urban water demand seriously. This paper proposes a Stacking-based hybrid model which integrates multi-correction mechanisms to address these problems. A better stacking model is proposed to minimize the generalization error. The stability and reliability of predictions are improved through the design of multi-correction mechanisms such as high temperature weather compensation feature, holiday-type correction model and water quantity fluctuation correction model. Comparing different models before and after stacking also before and after correcting, the prediction accuracy of the proposed hybrid model is much higher and the predictions are more stable and reliable.