Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …

This manuscript proposes a novel modified whale optimization algorithm-based solution
approach for congestion controlling cost problem in power system framework. The integral …

This paper proposes a method of load shedding in a microgrid system operated in an Island
Mode, which is disconnected with the main power grid and balanced loss of the electrical …

This paper presents a new optimization approach for solving the under-frequency load
shedding (UFLS) problem in power systems. UFLS is a very important function in …

Making accurate load-shedding decisions helps to reduce losses for customers and the
power system. This article proposes an improved Artificial Neural Network (ANN) application …

Apache spark, famously known for big data handling ability, is a distributed open-source
framework that utilizes the idea of distributed memory to process big data. As the …

Under-frequency load shedding (UFLS) is a critical function of frequency control in a power
system. This paper presents a new adaptive UFLS scheme in multi-area power systems …

It's essential to maintain a consistent and dependable energy supplies given the rising
global demand for energy. When there is a crisis, such as a malfunction or an imbalance …

The increased competitiveness makes traffic control on the electrical networks a major
concern. Because the electricity infrastructure is not governed by the government, traffic …

Load forecasting is a vital part of smart grids for predicting the required electrical power
using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the …