The growing success of smart grids (SGs) is driving increased interest in load forecasting
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …

Due to rapid development in information technology in both hardware and software, deep
neural networks for regression have become widely used in many fields. The optimization of …

Abstract Bioelectrochemical Systems (BESs) leverage microbial metabolic processes to
either produce electricity by degrading organic matter or consume electricity to assist …

The reversible pump‐turbine plays an important role in hydropower stations, but pressure
pulsation during their operation affects their performance and lifespan. Accurate prediction …

In order to further improve the efficiency of energy utilization, Integrated Energy Systems
(IES) connect various energy systems closer, which has become an important energy …

LSTM networks are popular for predicting data with nonlinear and temporal properties.
However, it is difficult to select optimal hyperparameters using empirical methods, which can …

Renewable energy resources can be deployed locally and efficiently using the concept of
microgrids. Due to the natural uncertainty of the output power of renewable energy …

The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is
increasing societal demand for electricity. The ability to integrate the additional demand from …

This paper proposes energy distribution in conditions of electricity shortage carried out by a
computer system using artificial intelligence (AI). The architecture of the data collection …

This paper proposes a fluid classifier for a tight reservoir using a quantum neural network
(QNN). It is difficult to identify the fluid in tight reservoirs, and the manual interpretation of …