Solar energy has been rapidly utilized in urban environments owing to its significant
potential to fulfill the energy demand. The precise forecasting of solar energy, including solar …

Advancements in renewable energy technology have significantly reduced the consumer
dependence on conventional energy sources for power generation. Solar energy has …

Although solar energy harnessing capacity varies considerably based on the employed
solar energy technology and the meteorological conditions, accurate direct normal …

As more and more photovoltaic (PV) systems are integrated into the grid, the intelligent
operation of the grid system is facing significant challenges. Therefore, accurately …

Forecasting the performance and energy yield of photovoltaic (PV) farms is crucial for
establishing the economic sustainability of a newly installed system. The present study aims …

Short-term performance degradation prediction is significant for fuel cell system control and
health management. This paper presents a hybrid deep learning method by combining the …

This study proposes a new hybrid deep learning (DL) model, the called CSVR, for Global
Solar Radiation (GSR) predictions by integrating Convolutional Neural Network (CNN) with …

Electricity load forecasting is the forecast of power load in the future period based on
historical load and its related factors. It is of great importance for power system planning …

Renewable energy (RE) offers major environmental and economic benefits compared to
nuclear and fuel-based energy; however, the data used for RE include significant …

Permanently increasing penetration of converter-interfaced generation and renewable
energy sources (RESs) makes modern electrical power systems more vulnerable to low …