Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection
Department of Computer Science, Systems and Communication, University of Milano-Bicocca, viale Sarca 336, 20126 Milan, Italy
Academic Editor: Ataur Rahman
Water 2017, 9(3), 224; https://doi.org/10.3390/w9030224
Received: 2 February 2017 / Revised: 6 March 2017 / Accepted: 10 March 2017 / Published: 18 March 2017
This paper presents a completely data-driven and machine-learning-based approach, in two stages, to first characterize and then forecast hourly water demand in the short term with applications of two different data sources: urban water demand (SCADA data) and individual customer water consumption (AMR data). In the first case, reliable forecasting can be used to optimize operations, particularly the pumping schedule, in order to reduce energy-related costs, while in the second case, the comparison between forecast and actual values may support the online detection of anomalies, such as smart meter faults, fraud or possible cyber-physical attacks. Results are presented for a real case: the water distribution network in Milan.
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Keywords:
time-series clustering; support vector regression; water demand forecasting; anomaly detection
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MDPI and ACS Style
Candelieri, A. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water 2017, 9, 224. https://doi.org/10.3390/w9030224
AMA Style
Candelieri A. Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection. Water. 2017; 9(3):224. https://doi.org/10.3390/w9030224
Chicago/Turabian StyleCandelieri, Antonio. 2017. "Clustering and Support Vector Regression for Water Demand Forecasting and Anomaly Detection" Water 9, no. 3: 224. https://doi.org/10.3390/w9030224
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