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Water Demand Prediction for Housing Apartments Using Time Series Analysis

Water Demand Prediction for Housing Apartments Using Time Series Analysis

Arpit Tripathi, Simran Kaur, Suresh Sankaranarayanan, Lakshmi Kanthan Narayanan, Rijo Jackson Tom
Copyright: © 2019 |Volume: 15 |Issue: 4 |Pages: 19
ISSN: 1548-3657|EISSN: 1548-3665|EISBN13: 9781522564355|DOI: 10.4018/IJIIT.2019100104
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MLA

Tripathi, Arpit, et al. "Water Demand Prediction for Housing Apartments Using Time Series Analysis." IJIIT vol.15, no.4 2019: pp.57-75. http://doi.org/10.4018/IJIIT.2019100104

APA

Tripathi, A., Kaur, S., Sankaranarayanan, S., Narayanan, L. K., & Tom, R. J. (2019). Water Demand Prediction for Housing Apartments Using Time Series Analysis. International Journal of Intelligent Information Technologies (IJIIT), 15(4), 57-75. http://doi.org/10.4018/IJIIT.2019100104

Chicago

Tripathi, Arpit, et al. "Water Demand Prediction for Housing Apartments Using Time Series Analysis," International Journal of Intelligent Information Technologies (IJIIT) 15, no.4: 57-75. http://doi.org/10.4018/IJIIT.2019100104

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Abstract

Water management has always been a topic of serious discussion since infrastructure, rural, and industrial development flourished. Due to the depleting water resources, this is now even a bigger challenge. So, here is developed an IoT-based water management system where ultrasonic sensors are employed for predicting the depth of water in the tank and accordingly pumping the water to the sub tank of the apartment. In addition, the time series analysis Auto Regressive Integrative Moving Average (ARIMA) and Least Square Linear Regression (LSLR) algorithms were employed and compared for predicting the water demand for next six months based on the historical water consumption record of the main reservoir/tank. The information on the amount of water consumed from the main reservoir is pushed to the cloud and to the mobile application developed for utilities. The purpose is to access the water consumption pattern and predict water demand for the next six months from the cloud.

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