A new optimal prediction technique for energy demand based on CNN and improved water strider algorithm: a study on socio-economic-climatic parameters

Rising demand and the potential decline in renewables resources have made energy production one of most important challenges future. In hydropower plants, various factors can be reasons to limit supply. this study, population GDP, climatic parameters (temperature precipitation) are analyzed for prediction demand. The innovation research is Introduction a developed algorithm called Improved Water Strider Algorithm (IWSA). results displayed that improved has faster convergence lowest error with value 1.55, 1.48, 1.44, 0.78 population, GDF, temperature, precipitation, respectively. This model highest correlation coefficient 0.77, 0.79, 0.8, 80 Also, 0.91, 0.47 best performance. Therefore, after confirming proposed method, predicted. showed among four input CNN-IWSA precipitation parameter significant effect on limiting increasing because it directly affect amount timing distribution. also show an trend next 20 years. These great help hydrologists managers controlling supplying energy.