[HTML][HTML] Advanced microstructure characterization and microstructural evolution of porous cermet electrodes in solid oxide cells: A comprehensive review
Solid oxide cells (SOCs), capable of interconverting electrical and chemical energy, have
emerged as one of the key technologies for the future multi-energy complementary grid …
emerged as one of the key technologies for the future multi-energy complementary grid …
Maximizing thermal integration performance in SOFC CHP systems: A top-down approach to configuration-parameter cooptimization
Despite significant interest in optimizing solid oxide fuel cell (SOFC) combined heat and
power (CHP) systems, a gap exists in systematic, top-down methods for refining …
power (CHP) systems, a gap exists in systematic, top-down methods for refining …
Hybrid model for robust and accurate forecasting building electricity demand combining physical and data-driven methods
The high-precision prediction of building electrical demand is paramount significance for
optimal control of building systems and the reduction of operational energy consumption …
optimal control of building systems and the reduction of operational energy consumption …
An improved deep temporal convolutional network for new energy stock index prediction
W Chen, N An, M Jiang, L Jia - Information Sciences, 2024 - Elsevier
Accurate prediction of the stock indexes in the new energy market is of significant
importance to both investors and policymakers. However, in response to the volatility and …
importance to both investors and policymakers. However, in response to the volatility and …
Scientometric analysis of research trends on solid oxide electrolysis cells for green hydrogen and syngas production
Solid oxide electrolysis cell (SOEC) is a promising water electrolysis technology that
produces hydrogen or syngas through water electrolysis or water and carbon dioxide co …
produces hydrogen or syngas through water electrolysis or water and carbon dioxide co …
A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism
Y Nie, P Li, J Wang, L Zhang - Applied Energy, 2024 - Elsevier
With the liberalization and deregulation of the power industry, the power market presents
increasingly invisible dynamics and uncertainty. Establishing an effective price forecasting …
increasingly invisible dynamics and uncertainty. Establishing an effective price forecasting …
Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network
A Meng, J Zhu, B Yan, H Yin - Applied Energy, 2024 - Elsevier
Factors such as high penetration of renewable energy, load, geographic location, and
interactions between price zones make accurate electricity price forecasting (EPF) very …
interactions between price zones make accurate electricity price forecasting (EPF) very …
Numerical multi-physical optimization of operating condition and current collecting setup for large-area solid oxide fuel cells
Due to the depletion of traditional fossil fuels and the aggravation of related environmental
problems, hydrogen energy is gaining more attention all over the world. Solid oxide fuel cell …
problems, hydrogen energy is gaining more attention all over the world. Solid oxide fuel cell …
A transformer-BILSTM based hybrid deep learning approach for day-ahead electricity price forecasting
Accurate electricity price forecasting in smart grids is critical for operational risk management
and optimal decision-making in bidding strategies in the day-ahead (DA) electricity markets …
and optimal decision-making in bidding strategies in the day-ahead (DA) electricity markets …
[HTML][HTML] XGBoost Based Enhanced Predictive Model for Handling Missing Input Parameters: A Case Study on Gas Turbine
This work extensively develops and evaluates an XGBoost model for predictive analysis of
gas turbine performance. The goal is to construct a robust prediction model by utilizing …
gas turbine performance. The goal is to construct a robust prediction model by utilizing …