Deep Learning-Based Prediction System of Energy Load from Solar and Wind Energy

Considering an ongoing global boost in interest in renewable energy solutions, it is evident that improving the accuracy and performance of prediction models is crucial for optimizing the renewable energy production process. The objective of this project is to create a very efficient deep learning-based prediction system that can determine the amount of energy produced by wind and solar power in various geographic areas. This model makes use of large data, including historical energy demands, meteorological data, and performance indicators of wind and solar power plants. Deep learning methods are used to harness the complex nature of the data. This model's methodology enables it to forecast energy generation with an extremely high level of accuracy. This predictive capacity is vital for regulating rising energy demands as well as to address the inherent risk involved in renewable resources. In the end, it provides a reliable and scalable technological solution that enables the smooth integration of renewable resources into modern power networks, encouraging the creation of sustainable energy infrastructure in line with international environmental goals.