Integration of Renewable Energy Sources to Smart Grid Using Machine Learning-Based Forecasting Models for Grid Stability Enhancement

With the increasing incorporation of renewable energy sources (RES) into power systems in today's world, advanced prediction techniques have become necessary to achieve grid security, economic efficiency and environmental sustainability. In this context, the present paper studies the integration of machine learning (ML) models based on longshort term memory and recurrent neural networks into smart grid operations. One particularly important aspect thereof is their capacity to enhance the forecastability and controllability for solar and wind power. A comprehensive comparison was carried out of five forecasting models-Linear Regression (LR), Support Vector Regression (SVR), Random Forest Regression (RFR), Artificial Neural Networks (ANN) and Long ShortTerm Memory (LSTM) networks-using real-world data imported into a Java-based smart grid simulation environment for IEEE-standard systems. Between them, it was found that LSTM is the most cost-effective model to use in financial terms: it has the lowest RMSE and Mean Absolute Percentage Error (MAPE) values out of any model (i.e., when predicting both sunshine hours and wind velocities it produces predictions closest to reality) with R² scores between 0.93, 0.91 for solar and wind power generation respectively, respectively. Its incorporation into the grid control framework resulted in significant improvements in grid stability: carrying out measured frequency oscillations replaced by frequency error less than 0.2 %, the network avoids voltage violations all together. Based on the rate of emergency intervention in major incidents for example power outages per year past twenty four this rate was more than eighty times lower. Meanwhile it also had a knock-on effect on economics-saving a net € 93,600 per year by adopting statistical forecasting model LSTM alone through reduced curtailment and balancing charges from generators. In addition, it led to substantial environmental benefits: the ensuing reduction resulted in an annual cutback of 910 tons CO2. Robustness and sensitivity analyses validated LSTM's reliability under less than ideal data conditions. Compared to traditional numerical weather prediction (NWP)based dispatch methods, the ML framework proposed here sharply improved forecast accuracy and grid performance. These results firmly position LSTM-based forecasting as essential for smart grid operations that integrate large levels of renewable energy. It makes the grid more resilient and economically efficient.