Recently, the popularity of using the expressive power of quantum computing to solve
known, challenging problems has increased remarkably. This study aims to develop a clear …

Intelligent predictive models are fundamental in peer-to-peer (P2P) energy trading as they
properly estimate supply and demand variations and optimize energy distribution, and the …

The transition to sustainable electricity generation depends heavily on renewable energy
sources, particularly wind power. Making precise forecasts, which calls for clever predictive …

This paper presents FARHAN, a novel hybrid model designed to address the challenges of
electrical load forecasting in smart grids. FARHAN combines descending neuron attention …

This paper studies the effect of the number of switching (NOS) per day of capacitor banks on
loss reduction in radial distribution systems. To this aim, the daytime (more precisely, 24 h) is …

Nowadays, the renewable energy sources (RESs) and distributed generation systems have
a major impact in the electrical energy market. But they suffer from some uncertainties …

Due to its capacity to handle data in fundamentally novel ways and produce computational
powers that were previously unreachable, the multidisciplinary subject of quantum …

In this paper, a neural network long short term memory hybrid model focusing on stock index
forecasting is modeled, presented, and investigated. This model is tuned by artificial …

The authors present the investigation of a new hybrid predictive model of Duplex Attention‐
based Coupled LSTM Markov Averaged Neural Network, known as DACLMANN. The …

The adoption of solar systems has witnessed a remarkable growth rate in recent years,
driven by increasing awareness of renewable energy and declining costs of solar …