Cited by Spatio-temporal photovoltaic prediction via a convolutional based hybrid network

A Long Short-Term Memory–Wasserstein Generative Adversarial Network-Based Data Imputation Method for Photovoltaic Power Output Prediction DOI

Zhu Liu,

Lingfeng Xuan,

Dehuang Gong

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(2), P. 399 - 399

Published: Jan. 17, 2025

To address the challenges of issue inaccurate prediction results due to missing data in PV power records, a photovoltaic imputation method based on Wasserstein Generative Adversarial Network (WGAN) and Long Short-Term Memory (LSTM) network is proposed. This introduces data-driven GAN framework with quasi-convex characteristics ensure smoothness imputed existing employs gradient penalty mechanism single-batch multi-iteration strategy for stable training. Finally, through frequency domain analysis, t-Distributed Stochastic Neighbor Embedding (t-SNE) metrics, performance validation generated data, proposed can improve continuity reliability tasks.

Language: Английский

Citations

An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction DOI

Jie Chen,

Peng Tian,

Shijie Qian

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124738 - 124738

Published: Oct. 22, 2024

Language: Английский

Citations

A WGAN-GP Approach for Data Imputation in Photovoltaic Power Prediction DOI

Zhu Liu,

Lingfeng Xuan,

Dehuang Gong

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(5), P. 1042 - 1042

Published: Feb. 21, 2025

The increasing adoption of photovoltaic (PV) systems has introduced challenges for grid stability due to the intermittent nature PV power generation. Accurate forecasting and data quality are critical effective integration into grids. However, records often contain missing system downtime, posing difficulties pattern recognition model accuracy. To address this, we propose a GAN-based imputation method tailored Unlike traditional GANs used in image generation, our ensures smooth transitions with existing by utilizing data-guided GAN framework quasi-convex properties. stabilize training, introduce gradient penalty mechanism single-batch multi-iteration strategy. Our contributions include analyzing necessity imputation, designing novel conditional network validating generated using frequency domain analysis, t-NSE, prediction performance. This approach significantly enhances continuity reliability tasks.

Language: Английский

Citations

Optimization of a Hybrid Solar Tower System for Power, Hydrogen, and Superheated Water Production DOI

Hadi Ghaebi,

Ghader Abbaspour

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 13, 2025

Abstract This research explores the incorporation of solar tower systems with a Thermal Energy Storage (TES) system in hybrid setup that includes supercritical S-CO₂ Brayton cycle, heat recovery steam generators (HRSGs) and Copper-Chlorine (Cu-Cl) cycle for producing hydrogen superheated steam. Energy, exergy, thermoeconomic examines are conducted to evaluate functionality each subsystem. TES helps mitigate fluctuations radiation by storing thermal energy periods lower input, proposed component is individually modeled utilizing Engineering Equation Solver (EES) software. In base case, The exergy destruction rates 9930 kW tower, 7111 9735 Cu-Cl cycle. generates $\:4226$ power, 2679 heating, $\:0.04971$ kg.s^{−

1} hydrogen, efficiencies 17.48% 18.72%. costs electricity, heat, production this case 0.2917, 0.1061, 0.02632 $/s, total cost 0.00003568 $/kJ.s. After optimization, 19.93% 21.35%, respectively, 5943 3268 0.06675 hydrogen. optimized 0.03193, 0.1222, 0.03337 reduced 0.00003193 These results highlight system's potential efficiency improvement, indicating notable economic operational benefits renewable applications.

Language: Английский

Citations

Assessing Solar-to-PV Power Conversion Models: Physical, ML, and Hybrid Approaches Across Diverse Scales DOI

Caixia Li, Yuanyuan Xu, Matthew H. Y. Xie

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135744 - 135744

Published: March 1, 2025

Language: Английский

Citations

Techno-Economic Implications and Cost of Forecasting Errors in Solar PV Power Production Using Optimized Deep Learning Models DOI

Sameer Al‐Dahidi, Mohammad Alrbai, Bilal Rinchi

et al.

Energy, Journal Year: 2025, Volume and Issue: unknown, P. 135877 - 135877

Published: March 1, 2025

Language: Английский

Citations

Multi-scale fused Graph Convolutional Network for multi-site photovoltaic power forecasting DOI

Qi Sima, Xinze Zhang,

Siyue Yang

et al.

Energy Conversion and Management, Journal Year: 2025, Volume and Issue: 333, P. 119773 - 119773

Published: April 17, 2025

Language: Английский

Citations

Framework design and empirical analysis of intelligent scheduling system for high-altitude photovoltaic power generation based on mixed optimization of long-nosed raccoon optimization algorithm and black winged kite optimization algorithm (COA-BKA) DOI

Heng Hu,

Xiaoming Xiong, Shuang Wang

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 28(1)

Published: April 25, 2025

Language: Английский

Citations

Enhancing multi-step short-term solar radiation forecasting based on optimized generalized regularized extreme learning machine and multi-scale Gaussian data augmentation technique DOI

Zheng Wang, Peng Tian, Xuedong Zhang

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124708 - 124708

Published: Oct. 19, 2024

Language: Английский

Citations

Spatio-temporal photovoltaic prediction via a convolutional based hybrid network DOI

Sicheng Wang, Yan Huang

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 123, P. 110021 - 110021

Published: Dec. 29, 2024

Language: Английский

Citations