Smart grids require intelligent and efficient energy management solutions, which is why there is a need to evolve the traditional power system. Correct load forecasting is critical to the optimum scheduling of generation, managing the load from customers' sides and maintaining system reliability. Conventional approaches, however, are not well suited for electrical loads that are both time-varying and nonlinear. This paper suggests a machine learning based method for local load forecasting and grid stability enhancement. Historical load and weather data are used to analyze and applied techniques like Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to improve the accuracy of predictions. Moreover, a predictive control technology is applied to ensure the system stability to reduce the voltage fluctuations, frequency deviation and loading imbalance. The proposed model dynamically adjusts control actions such as demand response and load shedding. The simulation results show lower forecasting errors (MAE, RMSE) and better voltage and frequency regulation to guarantee better reliability, efficiency, and stability of modern smart grid systems.

Keywords: Smart Grid; Machine Learning; Load Forecasting; Grid Stability; Predictive Control.