Abstract Nowcasting and forecasting solar irradiance are vital for the optimal prediction of grid-connected solar photovoltaic (PV) power plants. These plants face operational challenges and
Motivated by recent advancements in deep learning methods and their satisfactory performance in the energy sector, a hybrid deep learning model combining wavelet packet
Maximizing power generation efficiency and fault diagnosis are two important aspects for reducing the cost of energy (COE) for photovoltaic (PV) systems.
The comprehensive analysis demonstrates the superior adaptability of the proposed hybrid model compared with other prediction approaches, establishing its efficacy as a versatile and
Xiao Li Affiliation Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming, China Publication Topics
Thermal energy storage system in concentrating solar power plants can guarantee sustainable and stable electricity output in case of highly unstable solar irradiation conditions. In this...
The experimental results and simulations demonstrate that the proposed model can accurately estimate PV power generation in response to abrupt changes in power generation
To improve the comprehensive prediction performance of the model, this work proposes a reliable photovoltaic power prediction model based on neural-prophet (NP), convolutional neural
The output from the Performer layer is directly fed into the LSTM model to generate the final PV power generation prediction. We evaluated the performance of the model across three
The Solar Energy Crisis: Why Traditional Approaches Fall Short When Xiao Li organized her first solar power generation event in March 2023, she didn''t realize she''d uncovered a critical gap in renewable
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