DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 643, P. 131996 - 131996

Published: Sept. 16, 2024

Language: Английский

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Short-term PV-Wind forecasting of large-scale regional site clusters based on FCM clustering and hybrid Inception-ResNet embedded with Informer

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 320, P. 118992 - 118992

Published: Sept. 4, 2024

Language: Английский

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An adaptive distribution-matched recurrent network for wind power prediction using time-series distribution period division

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131383 - 131383

Published: April 25, 2024

Language: Английский

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Using Crested Porcupine Optimizer Algorithm and CNN-LSTM-Attention Model Combined with Deep Learning Methods to Enhance Short-Term Power Forecasting in PV Generation

Energies, Journal Year: 2024, Volume and Issue: 17(14), P. 3435 - 3435

Published: July 12, 2024

Due to the inherent intermittency, variability, and randomness, photovoltaic (PV) power generation faces significant challenges in energy grid integration. To address these challenges, current research mainly focuses on developing more efficient management systems prediction technologies. Through optimizing scheduling integration PV generation, stability reliability of can be further improved. In this study, a new model is introduced that combines strengths convolutional neural networks (CNNs), long short-term memory (LSTM) networks, attention mechanisms, so we call algorithm CNN-LSTM-Attention (CLA). addition, Crested Porcupine Optimizer (CPO) utilized solve problem generation. This abbreviated as CPO-CLA. first time CPO has been into LSTM for parameter optimization. effectively capture univariate multivariate series patterns, multiple relevant target variables patterns (MRTPPs) are employed CPO-CLA model. The results show superior traditional methods recent popular models terms accuracy stability, especially 13 h timestep. mechanisms enables adaptively focus most historical data future prediction. optimizes network parameters, which ensures robust generalization ability great significance establishing trust market. Ultimately, it will help integrate renewable reliably efficiently.

Language: Английский

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Impact Localization System of CFRP Structure Based on EFPI Sensors

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1091 - 1091

Published: Feb. 12, 2025

Carbon fiber composites (CFRPs) are prone to impact loads during their production, transportation, and service life. These impacts can induce microscopic damage that is always undetectable the naked eye, thereby posing a significant safety risk structural integrity of CFRP structures. In this study, we developed an localization system for structures using extrinsic Fabry–Perot interferometric (EFPI) sensors. The signals detected by EFPI sensors demodulated at high speeds intensity modulation method. An method structure based on energy–entropy ratio endpoint detection CNN-BIGRU-Attention proposed. time difference arrival (TDOA) between from different collected characterize location. attention mechanism integrated into CNN-BIGRU model enhance significance TDOA proximal trained training set, with its parameters optimized sand cat swarm optimization algorithm validation set. performance models then evaluated compared test was validated plate experimental area 400 mm × mm. average error in 8.14 mm, results demonstrate effectiveness satisfactory proposed

Language: Английский

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Time series modeling and forecasting with feature decomposition and interaction for prognostics and health management in nuclear power plant

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135784 - 135784

Published: April 1, 2025

Language: Английский

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Probabilistic prediction of wind farm power generation using non-crossing quantile regression

Control Engineering Practice, Journal Year: 2025, Volume and Issue: 156, P. 106226 - 106226

Published: Jan. 5, 2025

Language: Английский

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Short-Term Photovoltaic Power Prediction Based on Multi-Stage Temporal Feature Learning

Energy Engineering, Journal Year: 2025, Volume and Issue: 0(0), P. 1 - 10

Published: Jan. 1, 2025

Harnessing solar power is essential for addressing the dual challenges of global warming and depletion traditional energy sources.However, fluctuations intermittency photovoltaic (PV) pose its extensive incorporation into grids.Thus, enhancing precision PV prediction particularly important.Although existing studies have made progress in short-term prediction, issues persist, underutilization temporal features neglect correlations between satellite cloud images data.These factors hinder improvements performance.To overcome these challenges, this paper proposes a novel method based on multi-stage feature learning.First, improved LSTM SA-ConvLSTM are employed to extract spatial-temporal images, respectively.Subsequently, hybrid attention mechanism proposed identify interplay two modalities, capacity focus most relevant features.Finally, Transformer model applied further capture patterns long-term dependencies within multi-modal information.The also compares with various competitive methods.The experimental results demonstrate that outperforms methods terms accuracy reliability prediction.

Language: Английский

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Forecasting carbon price in Hubei Province using a mixed neural model based on mutual information and Multi-head Self-Attention

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144960 - 144960

Published: Feb. 1, 2025

Language: Английский

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A Three-stage Adjustable Robust Optimization Framework for Energy Base Leveraging Transfer Learning

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135037 - 135037

Published: Feb. 1, 2025

Language: Английский

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