Supervised Learning for Energy Forecasting in Power Systems

Since the inception of the electric grid in the 19th century, power systems have continuously evolved due to technological, industrial, legislative, demographic, environmental, and economic factors. With the advent of machine learning, monitoring and anticipating the evolutionary trends of the electric grid has become possible. This is facilitated by the convergence of vast data availability, sophisticated algorithms, and advanced computational capabilities. Our focus is on utilizing the supervised learning paradigm of machine learning for predictive analytics in power systems. Specifically, we aim to forecast electricity consumption, leveraging the predictive power of supervised learning techniques.