This study evaluates the effect of climate change on electricity generation, electricity demand, and GHG emissions. For this purpose, using climate scenarios RCPs changes of climatic parameters are predicted. Due to the high importance of energy demand in the management of energy generation resources innovation research is related to forecasting electricity demand. The novelty is the use of an Artificial Neural Network optimized to predict the energy demand. To optimize the ANN method, the Improved Pathfinder algorithm has been used. The use of the optimization method in the ANN method provides a model with more precision and fewer errors for the prediction of energy demand. The results showed that due to the weather changes, hydropower generation for the near future under RCP2.6, RCP4.5, and RCP8.5 increases by about 2.765 MW, 1.892 MW, and 1.219 MW and for the far future increases by about 3.430 MW, 2.475 MW, and 1.827 MW. The electricity demand forecasting by The ANN-IPF model for the near and far future will increase compared to the base period of 391.9 MW and 716.65 MW, respectively. Therefore, the gap between the demand the power supply will increase. Using other resources, the difference between demand and power supply will decrease. •The electricity demand has been stimulated by optimized ANN.•The hydropower generation has been predicted by RCPs scenarios.•The climate change impact on electricity supply and demand has been assessed.•The gap supply-demand electricity and the effect it on GHG emissions has been evaluated.•To reduce the supply-demand gap less -carbon resources should be used.