The world’s climate has changed dramatically in recent years due to the development of the industry. Climate change can significantly affect hydropower plants. One of the negative effects of climate change is the reduction of hydropower generation. Therefore, to better management of power supply and demand, the climate change effect on hydropower plants should be examined. The main purpose of this study is the prediction of future hydropower generation (2021–2050) in terms of climate change. One of the factors sensitive to climate change in hydropower plants is the amount of input flow to the reservoir. In this study, a method has been used that can increase the accuracy of flow estimation. This innovation is the use of the ANN model under the new version of the Developed Crow Search Algorithm (DCSA). This algorithm increases the accuracy of prediction by solving optimization disadvantages such as getting stuck in the optimal location, the imbalance between exploitation and exploration at different levels The results showed that the DCSA algorithm with minimum error (MSE = 1.06) and maximum correlation (R = 0.88) compared to other algorithms has the best performance. The results of the prediction of climate change under RCPs scenarios showed that the average annual power generation will decrease under RCP2.6 and RCP4.5 and RCP8.5 about 10.74% and% 16.38 and 22.25% respectively. Also, the average annual power generation under scenarios RCP2.6, RCP4.5, and RCP8.5 by 2050 are 740.33 MW, 603.12 MW, and 585.77 MW, respectively.

中文翻译：

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基于RCP和开发的Crow搜索优化算法的气候变化影响下的水力发电量预测

近年来，由于工业的发展，世界气候发生了巨大的变化。气候变化会对水力发电厂产生重大影响。气候变化的负面影响之一是水力发电量的减少。因此，为了更好地管理电力供需，应研究气候变化对水电站的影响。本研究的主要目的是根据气候变化预测未来水力发电（2021-2050）。水电站对气候变化敏感的因素之一是水库的输入流量。在这项研究中，使用了一种可以提高流量估计准确性的方法。这项创新是在新版本的开发乌鸦搜索算法（DCSA）下使用ANN模型。该算法通过解决卡在最优位置、不同层次开发与探索不平衡等优化弊端，提高了预测精度。结果表明，DCSA算法具有最小误差（MSE = 1.06）和最大相关性（R = 0.88）相比其他算法具有最好的性能。RCPs情景下的气候变化预测结果表明，RCP2.6、RCP4.5和RCP8.5下年均发电量将分别减少约10.74%和16.38%和22.25%。到2050年，RCP2.6、RCP4.5和RCP8.5情景下的平均年发电量分别为740.33MW、603.12MW和585.77MW。