Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 133, P. 108435 - 108435
Published: April 25, 2024
Language: Английский
Citations
15Significant wave height prediction based on variational mode decomposition and dual network model
Ocean Engineering, Journal Year: 2025, Volume and Issue: 323, P. 120533 - 120533
Published: Feb. 6, 2025
Language: Английский
Citations
1A Path Analysis—Generalized Method of Moments Based on a Nearest-Neighbor with Observed Variable Model for Developing New Scenario Policies to Reduce Greenhouse Gas Emissions from Agricultural Waste Towards Sustainability
Applied Sciences, Journal Year: 2025, Volume and Issue: 15(4), P. 2160 - 2160
Published: Feb. 18, 2025
This research aims to identify effective strategies for reducing greenhouse gas emissions from agricultural waste. It employs a quantitative approach using an advanced model, the Path Analysis—Generalized Method of Moments Based on Nearest-Neighbor with Observed Variable Model (Path-GMM-Nearest-Neighbor Model). model incorporates white noise and addresses gaps in previous models, ensuring minimal forecasting errors. The findings highlight need government implement most suitable policy scenario achieve sustained reductions waste over next two decades (2025–2044). Additionally, we found that Path-GMM-Nearest-Neighbor demonstrated highest performance, exhibiting lowest Mean Absolute Percentage Error (MAPE) Root Squared (RMSE). Following descending order, were GM-ARIMA Model, Fuzzy BP ANN Regression Model. optimal indices identified are green technology biomass energy. Implementing these national administration is projected reduce growth rate only 50.58% (2044/2025) while continuously decreasing emissions, expansion limited 43.68% (2044/2025). These measures ensure remain below Thailand’s carrying capacity threshold 1560 Gg CO2e. Thus, adopting this strategy as will enable Thailand sustainably advance toward economy future.
Language: Английский
Citations
1A hybrid PM2.5 interval concentration prediction framework based on multi-factor interval decomposition reconstruction strategy and attention mechanism
Atmospheric Environment, Journal Year: 2024, Volume and Issue: 335, P. 120730 - 120730
Published: Aug. 7, 2024
Language: Английский
Citations
4Hybrid point-interval prediction method for stochastic dynamic response of subsea umbilical cable based on BO-BiLSTM and adaptive bandwidth KDE
Ocean Engineering, Journal Year: 2025, Volume and Issue: 320, P. 120317 - 120317
Published: Jan. 10, 2025
Language: Английский
Citations
0Motion interval prediction of a sea satellite launch platform based on VMD-QR-GRU
Ocean Engineering, Journal Year: 2024, Volume and Issue: 312, P. 119005 - 119005
Published: Aug. 30, 2024
Language: Английский
Citations
3Solving the temporal lags in local significant wave height prediction with a new VMD-LSTM model
Ocean Engineering, Journal Year: 2024, Volume and Issue: 313, P. 119385 - 119385
Published: Oct. 7, 2024
Language: Английский
Citations
3CMLLM: A novel cross-modal large language model for wind power forecasting
Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 330, P. 119673 - 119673
Published: Feb. 27, 2025
Language: Английский
Citations
0Significant Wave Height Prediction Based on Improved Fuzzy C-Means Clustering and Bivariate Kernel Density Estimation
Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122787 - 122787
Published: March 1, 2025
Language: Английский
Citations
0Research on short-term photovoltaic power point-interval prediction method based on multi-scale similar day and EVO-TABiGRU
Measurement Science and Technology, Journal Year: 2025, Volume and Issue: 36(4), P. 046011 - 046011
Published: April 4, 2025
Abstract Photovoltaic (PV) power generation, known for its environmental benefits and renewability, plays a critical role in advancing sustainable energy. However, the inherent randomness volatility of PV generation challenge stable operation systems with high penetration. Accurate prediction is essential ensuring safe grid integration reliable system operation. This study introduces an advanced short-term framework, combining multi-scale similar days (MSSD) selection trend-aware bidirectional gated recurrent unit (TABiGRU). First, MSSD employed to select historical data meteorological conditions predicted day as training samples, reducing impact on model. Then, enhance model’s ability capture trends dynamics, TABiGRU model proposed, which change rate features dynamic weight adjustment improve adaptability fluctuations. In addition, energy valley optimization algorithm used tune hyperparameters TABiGRU, preventing performance degradation due improper parameter settings. Furthermore, mitigate cumulative error issue point under uncertain conditions, adaptive bandwidth kernel density estimation generate high-quality intervals, providing more robust decision support scheduling. Finally, experimental results demonstrate that proposed method achieves accuracy stability various particularly showing significant advantages complex fluctuation scenarios, strong grid.
Language: Английский
Citations
0