Urban Water-Energy consumption Prediction Influenced by Climate Change utilizing an innovative deep learning method - Dianli Wang; Yu Zhang; Nasser Yousefi (Scientific Reports)
Abstract The growing global demand for water and energy has created an urgent necessity for precise forecasting and management of these resources, especially in urban regions where population growth and economic development are intensifying consumption. Shenzhen, a rapidly expanding megacity in China, exemplifies this trend, with its water and energy requirements anticipated to rise further in the upcoming years. This research proposes an innovative Convolutional Neural Network (CNN) technique for forecasting water and energy consumption in Shenzhen, considering the intricate interactions among climate, socio-economic, and demographic elements. The proposed approach integrates a CNN model with an Enhanced Gorilla Troops Optimization (EGTO) algorithm to demonstrate superior performance compared to other leading methods in terms of accuracy and reliability. The results show a strong correlation between the simulated and observed data, with a correlation coefficient of 0.87 for water consumption and 0.91 for energy consumption, indicating a high level of agreement between the simulated and real-world data. Also, it is indicated that the new technique can accurately forecast water and energy consumption, achieving a mean absolute error (MAE) of 0.63 and a root mean square error (RMSE) of 0.58, respectively. The research indicates that the suggested approach can promote policymakers and stakeholders in making well-informed decisions by delivering precise predictions of water and energy usage. This, in turn, can facilitate better resource distribution, minimize waste, and promote greater sustainability. The study emphasizes the necessity of incorporating climate change and socio-economic factors into the forecasting process and showcases the proposed method’s potential to aid decision-making in this domain.