With increasing demand for energy, the penetration of alternative sources such as
renewable energy in power grids has increased. Solar energy is one of the most common …

The need for data-driven technologies such as artificial intelligence (AI), machine learning
(ML), and deep learning (DL) in various sectors has been soaring for over a decade. The …

Rapidly varying load demand is one of the greatest problems that distribution system
operators are now experiencing. Many researchers have been implemented the load …

Forecasting has always been the backbone of planning studies in the power system. With
the advent of the restructured power system and also the integration of renewable energy …

One of the biggest challenge that distribution system operators are facing today is
unpredictable load demand. Various research methodologies including deterministic as well …

This paper focuses on short-term load forecasting for the day ahead using an Ensemble
learning-based Random Forest method. The study uses real-time hourly load data and …

Activities related to the planning and operation of power systems use premise load
forecasting, which is responsible for providing a load estimative for a given horizon that …

This paper deals with the study of a prediction algorithm based on bootstrap aggregation
technique applied to short term load forecasting problem. Short term Load forecasting is a …

Activities related to the planning and operation of power systems use as premise the load
forecasting, which is responsible to provide a load estimative for a given horizon that assists …

Predicting electrical load is crucial for microgrid energy management. Short-term load
forecasting (STLF) helps in optimizing energy management and load balancing within …