Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model

• Main Authors: Li-Na Guo, Chen She, De-Bin Kong, Shuai-Ling Yan, Yi-Peng Xu, Majid Khayatnezhad, Fatemeh Gholinia
• Format: Article
• Language: English
• Published: Elsevier 2021-11-01
• Series: Energy Reports
• Subjects: The climatic parameters; Artificial neural network; The improved electromagnetic field optimization (IEFO) algorithms; Hydropower generation; The greenhouse gas emission
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352484721007368

In this study, an attempt is made to manage the gap between energy demand and energy supply by predicting hydropower production, energy demand, and greenhouse gas emissions. The interaction between climatic, hydrological, and socio-economic parameters creates a nonlinear and uncertain relationship. The complexity of this nonlinear relationship necessitate the use of ANN to estimate energy demand. To predict energy demand, ANN model is used along with improved Electromagnetic Field Optimization (IEFO) algorithms. The results show, hydroelectric generation in the near future under RCP2.6, RCP4.5, and RCP8.5 is decreased 10.981 MW, 12.933MW, and 14.765MW and in the far future decreased 21.922 MW, 23.649 MW, and 26.742 MW. The energy demand increases in the near future 513 MW and far future 1168 MW. According to forecasting hydropower generation and energy demand, the gap between the demand-supply will increase. Also, the greenhouse gases emissions is increase due to the increase in fossil fuel consumption.