MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net DOI Open Access

Q. Zhuang,

Lu Gao,

Fei Zhang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(23), P. 4829 - 4829

Published: Dec. 6, 2024

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect power generation, the uncertainty instability of above bring challenges to regulation control which requires flexible management scheduling strategies. Therefore, it is crucial improve accuracy ultra-short-term prediction. To solve this problem, paper proposes an prediction method with MIVNDN. Firstly, Spearman’s Kendall’s correlation coefficients are integrated select appropriate features. Secondly, multi-strategy dung beetle optimization algorithm (MSDBO) used optimize parameter combinations in improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) method, optimized decompose historical sequence obtain a series intrinsic modal function (IMF) components different frequency ranges. Then, high-frequency band IMF low-frequency reconstructed using t-mean test sample entropy, component decomposed quadratically variational (VMD) new set components. Finally, Nons-Transformer model by adding dilated causal convolution its encoder, components, as well unreconstructed mid-frequency IMF, inputs results perform error analysis. The experimental show that our proposed outperforms single combined models.

Language: Английский

A new paradigm based on Wasserstein Generative Adversarial Network and time-series graph for integrated energy system forecasting DOI
Zhirui Tian, Gai Mei

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 326, P. 119484 - 119484

Published: Jan. 13, 2025

Language: Английский

Citations

2
Recurrent Fourier-Kolmogorov Arnold Networks for photovoltaic power forecasting DOI Creative Commons
Desheng Rong, Zhongbao Lin,

Guomin Xie

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 8, 2025

Accurate day-ahead forecasting of photovoltaic (PV) power generation is crucial for system scheduling. To overcome the inaccuracies and inefficiencies current PV models, this paper introduces Recurrent Fourier-Kolmogorov Arnold Network (RFKAN). Initially, recurrent kernel nodes are employed to investigate interdependencies within sequences. Subsequently, Fourier series applied extract periodic features, enhancing accuracy training speed. Ablation studies conducted using data from a plant in Tieling City, Liaoning Province, validate effectiveness these two structural enhancements. Comparative experiments with baseline state-of-the-art models further underscore efficiency RFKAN. The results indicate that RFKAN achieves best performance grid depth 100 an input sequence length 2, reducing RMSE MAE by at least 5%, increasing CORR 2%, decreasing time 24% compared advanced models.

Language: Английский

Citations

1
A short-term wind power prediction based on MCOOT optimized deep learning networks and attention-weighted environmental factors for error correction DOI
Yiping Xiao, Honghao Wei, Shi Ying

et al.

Energy, Journal Year: 2025, Volume and Issue: 324, P. 136054 - 136054

Published: April 23, 2025

Language: Английский

Citations

0
Probability density function based adaptive ensemble learning with global convergence for wind power prediction DOI
Jianfang Li, Jia Li, Chengyu Zhou

et al.

Energy, Journal Year: 2024, Volume and Issue: 312, P. 133573 - 133573

Published: Nov. 1, 2024

Language: Английский

Citations

1
The short-term wind power prediction based on a multi-layer stacked model of BO-CNN-BiGRU-SA DOI
Wen Chen, Huang Hong-quan,

Xingke Ma

et al.

Digital Signal Processing, Journal Year: 2024, Volume and Issue: 156, P. 104838 - 104838

Published: Nov. 7, 2024

Language: Английский

Citations

1
MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net DOI Open Access

Q. Zhuang,

Lu Gao,

Fei Zhang

et al.

Electronics, Journal Year: 2024, Volume and Issue: 13(23), P. 4829 - 4829

Published: Dec. 6, 2024

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect power generation, the uncertainty instability of above bring challenges to regulation control which requires flexible management scheduling strategies. Therefore, it is crucial improve accuracy ultra-short-term prediction. To solve this problem, paper proposes an prediction method with MIVNDN. Firstly, Spearman’s Kendall’s correlation coefficients are integrated select appropriate features. Secondly, multi-strategy dung beetle optimization algorithm (MSDBO) used optimize parameter combinations in improved complete ensemble empirical mode decomposition adaptive noise (ICEEMDAN) method, optimized decompose historical sequence obtain a series intrinsic modal function (IMF) components different frequency ranges. Then, high-frequency band IMF low-frequency reconstructed using t-mean test sample entropy, component decomposed quadratically variational (VMD) new set components. Finally, Nons-Transformer model by adding dilated causal convolution its encoder, components, as well unreconstructed mid-frequency IMF, inputs results perform error analysis. The experimental show that our proposed outperforms single combined models.

Language: Английский

Citations

1