AI-Powered Energy Forecasting: ANext-Gen Approach for Smart Grids and Sustainable Power Systems

الملخص

Integrating artificial intelligence (AI) into energy forecasting has become an important component in developing smart grids and improving renewable energy systems. This research paper presents a next-generation artificial intelligence-driven approach to predicting the production and consumption of renewable energy with a special focus on solar and wind energy systems. The main objective of this study is to explore how artificial intelligence models, including machine learning algorithms such as ARIMA (autoregressive integrated moving average), long short-term memory (LSTM) networks, and regression models, can increase the accuracy and reliability of energy predictions in real time. Through experiments with publicly available energy consumption and weather data, research compares the performance of various artificial intelligence techniques for energy forecasting, focusing on their ability to predict energy production and improve grid management. The results show that artificial intelligence powered models perform better than traditional methods by improve prediction accuracy and enable renewable resources to be better integrated into power grids. Furthermore, the scalability of artificial intelligence models and their ability to transform energy systems making them more sustainable and efficient. The results provide valuable insight into the future role of artificial intelligence in enhancing the resilience and intelligence of energy networks, which contribute to the global transition toward cleaner and more reliable power sources.