Direct current (DC) microgrids are becoming increasingly important due to a number of
causes, including the widespread use of DC loads, the integration of solar photovoltaic (PV) …

This paper presents the application of Particle Swarm Optimization (PSO) Algorithm, Artificial
Neural Networks (ANNs) and Bagged Tree (BT) methods for forecasting seasonal solar …

This work presents the first version of DL 2 F, a powerful Deep Learning model specifically
designed to forecast four essential weather variables that influence solar power potential …

Heat-exchange station is the key mid-link between heat source and heat users in district
heating system, playing a role in distributing heat and regulating supply and demand …

DC microgrids are currently experiencing a surge in attention and interest, emerging as a
focal point in the global energy discourse due to their potential to enhance energy efficiency …

Accurate prediction of the average thermal extraction load (ATEL) in hydrothermal heating
systems optimizes energy recovery, though numerical models are constrained by modeling …

Many researchers have examined the benefits of machine learning (ML) algorithms in
geothermal drilling, especially for predicting the rate of penetration (ROP) of drilling …

Solar energy is an unlimited and sustainable energy source that holds great importance
during the global shift towards environmentally friendly energy production. However …

Solar radiation has important impacts on buildings such as for cooling/heating load
forecasting, energy consumption forecasting, and multi-energy complementary optimization …

Solar radiation is a fundamental parameter for solar photovoltaic (PV) technology. Reliable
solar radiation prediction has become valuable for designing solar PV systems …