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Differential Learning for Outliers: A Case Study of Water Demand Prediction

1
Department of Electrical and Computer Engineering, Purdue School of Engineering and Technology, Indiana University Purdue University, Indianapolis, IN 46202, USA
2
Citizens Energy Group, Indianapolis, IN 46202, USA
*
Author to whom correspondence should be addressed.
Appl. Sci. 2018, 8(11), 2018; https://doi.org/10.3390/app8112018
Received: 29 September 2018 / Revised: 15 October 2018 / Accepted: 18 October 2018 / Published: 23 October 2018
Predicting water demands is becoming increasingly critical because of the scarcity of this natural resource. In fact, the subject was the focus of numerous studies by a large number of researchers around the world. Several models have been proposed that are able to predict water demands using both statistical and machine learning techniques. These models have successfully identified features that can impact water demand trends for rural and metropolitan areas. However, while the above models, including recurrent network models proposed by the authors are able to predict normal water demands, most have difficulty estimating potential deviations from the norms. Outliers in water demand can be due to various reasons including high temperatures and voluntary or mandatory consumption restrictions by the water utility companies. Estimating these deviations is necessary, especially for water utility companies with a small service footprint, in order to efficiently plan water distribution. This paper proposes a differential learning model that can help model both over-consumption and under-consumption. The proposed differential model builds on a previously proposed recurrent neural network model that was successfully used to predict water demand in central Indiana. View Full-Text
Keywords: water demand; outlier; prediction; recurrent neural network water demand; outlier; prediction; recurrent neural network
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Shah, S.; Ben Miled, Z.; Schaefer, R.; Berube, S. Differential Learning for Outliers: A Case Study of Water Demand Prediction. Appl. Sci. 2018, 8, 2018.

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