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Smart city article details

Title	Sw Forecaster: An Intelligent Data-Driven Approach For Water Usage Demand Forecasting
ID_Doc	54126
Authors	Ubaid A.; Lin X.; Hussain F.K.
Year	2024
Published	Proceedings of the International Conference on Cloud Computing Technology and Science, CloudCom
DOI	http://dx.doi.org/10.1109/CloudCom62794.2024.00016
Abstract	Short-term water demand prediction is essential for optimizing residential and industrial water management. Several studies have demonstrated the usefulness of water usage demand forecasting in making smart cities sustainable. However, the real-world translation of such forecasting systems still needs to be exploited. In this research, we have developed +10 days short-term demand forecasting framework that utilizes the existing state-of-the-art statistical and deep learning models. The designed framework has been integrated into the utility company's legacy water usage demand forecasting process to promote digitization and sustainability. The research outcomes have demonstrated that the designed framework has improved the forecast accuracy upon successful integration with the legacy operational process. Index Terms-Short-term Forecasting, Time Series Modeling, Regression Modeling, Deep Learning Modeling, Water Demand, Water Supply, Weather-based Demand Prediction © 2024 IEEE.
Author Keywords	Deep Learning Modeling; Regression Modeling; Short-term Forecasting; Time Series Modeling; Water Demand; Water Supply; Weather-based Demand Prediction

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