AI Powered Renewable Energy Balancing, Forecasting and Global Trend Analysis using ANN-LSTM Integration

The instability of renewable energy sources like solar and wind places significant hurdles on energy distribution and grid stability, thus hampering the race towards sustainable energy solutions. These instabilities, mainly due to fluctuating weather conditions, may lead to surpluses or shortages of energy-with inevitable effects on the grid's reliability. It is proposed that an AI-enabled system based on ANN and LSTM solutions be developed to analyse global energy trends, predict renewable generation accurately, and enhance the grid's resilience. The new model resides on the historical and real-time energy data and adequately captures the long-range transition of energy and the short-range fluctuations in energy, allowing better energy management. Along with that, the intelligent forecasting will also optimize energy storage and minimize the overreliance on normal fossil fuel energy. The insights drawn out by this model provide considerable assistance to decision-makers, energy suppliers, and grid operators in their drive for a more stable, efficient, dependable, and sustainable energy infrastructure. This research highlights the significant role that AI-driven predictive analytics should play in facilitating global transitions toward renewable energy while addressing some of the critical operational challenges to grid reliability and energy distribution.