Game-Theoretic Optimization of National Energy Strategies Using Fbprophet Forecasting

The surging need for global energy creates huge challenges for efficient, sustainable management of energy. This work proposes a new framework combining game theory with Facebook Prophet (Fbprophet) time-series forecasting to optimize the distribution and consumption of energy at a country level. This proposed framework utilizes the capability of Fbprophet to forecast the energy demand accurately using past consumption data as well as seasonal patterns, while game-theoretic models model the strategic interactions among producers, distributors, and consumers to determine optimal resource allotment. Experimental results show that the proposed hybrid method greatly improves the accuracy of demand forecasting, operating efficiency, as well as its ability to adapt with renewable integration. The proposed system provides a scalable solution for improving the reliability of the grid, eliminating energy wastage, reducing operating expenditures, as well as enhancing environmental sustainability. This research provides a stepping stone for intelligent, data-driven-based energy management, leading the way towards incorporation of IoT, AI, as well as dynamic pricing models to enable smart grid development.