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

The consumed electrical load is affected by many external factors such as weather, season
of the year, weekday or weekend and holiday. In this paper, it is tried to provide a high …

Accurate load forecasting guarantees the stable and economic operation of power systems.
With the increasing integration of distributed generations and electrical vehicles, the …

Nowadays, supplying demand load and maintaining sustainable energy are important
issues that have created many challenges in power systems. In these types of problems …

Aiming at the continuous, periodic, and nonlinear characteristics of load changes, this paper
proposes a combined ultra-short-term load forecasting model based on improved complete …

In this article, a new approach to short-term load forecasting is proposed using a
multicolumn radial basis function neural network (MCRN). The advantage of this new …

Accurate power load forecasting is of great significance to ensure the safety, stability, and
economic operation of the power system. In particular, short‐term power load forecasting is …

Short‐term load forecasting (STLF) plays a very important role in improving the economy
and stability of the power system operation. With the smart meters and smart sensors widely …

The reform of power market has presented new challenges to short‐term load forecasting
(STLF), and the accuracy of forecast results is of great significance to the orderly and …

Short-term load forecasting is a critical task in the smart grid, which can be used to optimize
power deployment and reduce power losses. Recurrent neural networks (RNNs) are the …