标题： 一种面向可持续海水淡化的需求响应人工智能流程：阿联酋见解 显示英文标题

标题： A Feed-Forward Artificial Intelligence Pipeline for Sustainable Desalination under Climate Uncertainties: UAE Insights

摘要： 阿拉伯联合酋长国（UAE）严重依赖海水淡化来满足其超过90%的饮用水需求。 淡化过程能耗极高，占UAE用电量的约15%，导致该国与能源相关的二氧化碳排放量超过22%。 此外，面对气候变化带来的不确定性，如海水温度上升、盐度增加和气溶胶光学深度（AOD），这些过程面临着重大的可持续性挑战。 AOD通过光伏污染、膜污染和水浊度循环显著影响太阳能驱动淡化系统的运行和经济性能。 本研究提出了一种创新的分阶段预测建模架构：第一阶段使用卫星衍生的时间序列和气象数据预测AOD；第二阶段利用预测的AOD和其他气象因素预测淡化性能效率损失。 该框架达到了98%的准确性，SHAP（SHapley Additive exPlanations）被用来揭示系统退化的关键驱动因素。 此外，本研究基于AOD和太阳能效率的预测值，提出了一个考虑灰尘的基于规则的控制逻辑用于淡化系统。 该控制逻辑用于调整淡化厂进水压力，适应维护计划，并调节能源源切换。 为了增强研究成果的实际应用价值，预测模型和基于规则的控制被封装成一个交互式仪表板，用于情景和预测分析。 这为气候适应性规划提供了管理决策支持系统。 显示英文摘要

摘要： The United Arab Emirates (UAE) relies heavily on seawater desalination to meet over 90% of its drinking water needs. Desalination processes are highly energy intensive and account for approximately 15% of the UAE's electricity consumption, contributing to over 22% of the country's energy-related CO2 emissions. Moreover, these processes face significant sustainability challenges in the face of climate uncertainties such as rising seawater temperatures, salinity, and aerosol optical depth (AOD). AOD greatly affects the operational and economic performance of solar-powered desalination systems through photovoltaic soiling, membrane fouling, and water turbidity cycles. This study proposes a novel pipelined two-stage predictive modelling architecture: the first stage forecasts AOD using satellite-derived time series and meteorological data; the second stage uses the predicted AOD and other meteorological factors to predict desalination performance efficiency losses. The framework achieved 98% accuracy, and SHAP (SHapley Additive exPlanations) was used to reveal key drivers of system degradation. Furthermore, this study proposes a dust-aware rule-based control logic for desalination systems based on predicted values of AOD and solar efficiency. This control logic is used to adjust the desalination plant feed water pressure, adapt maintenance scheduling, and regulate energy source switching. To enhance the practical utility of the research findings, the predictive models and rule-based controls were packaged into an interactive dashboard for scenario and predictive analytics. This provides a management decision-support system for climate-adaptive planning.