Renewable Energy Systems Using Game Theory For Power Forecasting Energy Simulation

Energy internet is an open platform that connects all the devices involved in the power supply chain, from generation to distribution to consumption, and everything in between. In this study, we explore the widespread problem of synchronized control of conventional and renewable energy by combining game theory and machine learning techniques. We model the problems of energy management as a three-stage Stackelberg game, and use the backward induction technique to provide closed-form representations of optimal solutions. Following an examination of machine learning-based power generation forecasting methods, we provide a wind energy prediction method that may be useful in formulating a strategy for renewable energy generation. Numerical simulations show that better wind power projection estimates allow for more efficient use of generated electricity.