Gated Recurrent Unit for Load Forecasting of Water Pumping Stations in Jebel Akhdar

Abstract

The relationship between water demand and electrical power consumption is critical as water transmission systems necessitate considerable amounts of energy. Accurate load forecasting for water pumping stations can improve the proper administration of energy, reduce inefficiency, and improve profitability. The application of contemporary deep learning techniques can significantly optimize energy consumption, save expenditures, and promote sustainable development in the context of water pumping stations. Moreover, precise load forecasting is essential for the proper functioning and energy management of water pumping stations, especially in areas with intricate topographical circumstances. Hence, this research utilizes Gated Recurrent Units (GRUs) to forecast the load demands of water pumping stations in Jebel Akhdar. The suggested model is specifically intended to capture the temporal dependencies and non-linear patterns that are inherent in the load demand data of the water pumping stations. In this regard, GRUs excel in their ability to dynamically update the hidden state, allowing them to capture complex temporal patterns accurately. Therefore, this study offers specific insights and solutions that may be used to comparable places characterized by intricate time-series variables. The approach provides superior prediction accuracy compared to standard forecasting methods by using historical load data. The findings of this work illustrate valuable insights for utility regulators to optimize energy usage and ensure sustainable water delivery.

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