Forecasting mooring tension of offshore platforms based on complete ensemble empirical mode decomposition with adaptive noise and deep learning network

Measurement, Journal Year: 2024, Volume and Issue: 239, P. 115515 - 115515

Published: Aug. 15, 2024

Language: Английский

Solar irradiation forecast enhancement using clustering based CNN-BiLSTM-attention hybrid architecture with PSO

International Journal of Ambient Energy, Journal Year: 2024, Volume and Issue: 45(1)

Published: Oct. 17, 2024

Accurate solar irradiation forecasting is essential for optimising energy use. This paper presents a novel approach: the 'Clustering-based CNN-BiLSTM-Attention Hybrid Architecture with PSO'. It combines clustering, attention mechanisms, Convolutional Neural Networks (CNN), Bidirectional Long-Short Term Memory (BiLSTM) networks, and Particle Swarm Optimisation (PSO) into unified framework. Clustering categorises days groups, improving predictive capabilities. The CNN-BiLSTM model captures spatial temporal features, identifying complex patterns. PSO optimises hybrid model's hyperparameters, while an mechanism assigns probability weights to relevant information, enhancing performance. By leveraging patterns in data, proposed improves accuracy univariate multivariate analyses multi-step predictions. Extensive tests on real-world datasets from various locations show effectiveness. For example, NASA power achieves Mean Absolute Error (MAE) of 24.028 W/m2, Root Square (RMSE) 43.025 R2 score 0.984 1-hour ahead forecasting. results significant improvements over conventional methods.

Language: Английский

Data Decomposition Strategy to Improve Solar Forecasting Accuracy

Published: Nov. 28, 2023

Language: Английский