Short-term forecasting of hourly water consumption by using automatic metering readers data. (2015)

• Record Type: Journal Article
• Authors: Candelieri, Antonio; Soldi, Davide; Archetti, Francesco
• Is Part Of: Procedia engineering. Volume 119(2015)
• Page Start: 844
• Page End: 853
• Publication Date: 2015
• Subjects: Short-term demand forecasting -- Time-series mining -- Time-series clustering -- Support Vector Machines regression; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Conference proceedings; Periodicals; 620.005
• DOI: 10.1016/j.proeng.2015.08.948
• Languages: English
• ISSNs: 1877-7058

Abstract

Abstract: A completely data-driven, fully adaptive self-learning algorithm for water demand forecasting in the short-term and with hourly periodicity is proposed, according to the renewed interest generated by the availability of new technological solutions such as Automatic Metering Readers (AMR), a key enabler of the "Smart Water" paradigm. The approach is based on two sequential stages: at the first stage (time-series clustering) the daily water demand patterns (i.e., time-series of hourly data) are analysed to identify a limited set of typical behaviours. At the second stage Support Vector Machine regression is used to obtain one specific forecasting model (consisting of a regression model for each hour) for each cluster identified at the first stage. The approach has been validated on real data acquired by AMRs deployed on the Italian pilot site of ICeWater, computing the widely adopted error measure MAPE (Mean Absolute Percentage Error).