With the rapid development of smart grids and distributed energy sources, the home energy
management system (HEMS) is becoming a hot topic of research as a hub for connecting …

Existing short-term bus load forecasting methods mostly use temporal domain features, such
as historical loads, to forecast and do not fully consider the influence of unstructured spatial …

In this paper a dynamic Artificial Neural Network (ANN) model called Meta-ANN is
developed for forecasting the short-term grid load. The primary ingredient of the model is a …

Very short-term load forecasting (VSLTF) plays an essential role in guaranteeing effective
electricity dispatching and generating in residential microgrid systems. However, the …

This paper proposes a novel clustering and dynamic recognition–based auto-reservoir
neural network (CDbARNN) for short-term load forecasting (STLF) of industrial park …

As the power grid becomes more complex and dynamic, accurate short-term load
forecasting (STLF) with probabilistic information is a prerequisite for various smart grid …

Numerous studies on short‐term load forecasting (STLF) have used feature extraction
methods to increase the model's accuracy by incorporating multidimensional features …

Accurate multi-node load forecasting is the key to the safe, reliable, and economical
operation of the power system. However, the dynamic nature of load and the coupling nature …

Spinning reserve without accurate load forecasting can lead to automatic disconnection of
critical loads by under-frequency load shedding devices. Such a predicament poses a grave …

Forecasting short-term electricity load (STEL) is a very important but challenging task by the
fact that the series dynamic change involves in multiple patterns, such as long short-term …