Deep Learning Models for Predictive Stability in Renewable Energy

The usage of renewable energy sources is changing the distribution of energy. This makes customers both energy users and producers, which means that energy flows both ways in smart grids. In this complicated system, keeping the balance between supply and demand in check is necessary for keeping the grid safe. Researchers are currently focusing on decentralized smart grid control (DSGC) systems that measure grid frequency, which is a key indicator of power balance, to get a real-time idea of how stable the system is. Using math equations, the DSGC model predicts grid stability in a simple 4node star structure. That being said, this model is too simple and needs more complex approaches. To make a better analysis, this study uses deep learning methods, such as Keras' sequential model. For prediction purposes, the model includes 60,000 examples and 12 predictive features. These include reaction time, nominal power, and price elasticity. Cross-validation can be used to create an artificial neural network (ANN) with three hidden layers after scaling. When it came to predicting grid safety, this model had an accuracy of 97.73% compared to simpler models.