Application of Machine Learning Algorithms for Short term Load Prediction of Smart grid

Machine learning is making waves in- Energy Industry. The combination of Internet of Things (IOT) and big data leads to large collection of data which can be used for getting some insights and take intelligent decision using machine learning algorithms. Load prediction ensures the higher performance of opera-tion of Smart grid besides providing uninterrupted power supply to the consumer. In this paper Machine Learning algorithms are implemented for accurate load forecasting as it optimizes various deci-sion making to improve the efficiency of operation in smart grid. ML algorithms such as Neural Networks, Radial Basis Function (RBF), Decision Tree Regres-sion (DTR) and Random Forest are implemented in TensorFlow for load forecasting and comparison is made.