An Integrated Scheme for Forecasting and Controlling Ramps in Offshore Wind Farms Considering Wind Power Uncertainties during Extreme Storms DOI Open Access

Yongyong Jia,

Bixing Ren, Qiang Li

et al.

Electronics, Journal Year: 2023, Volume and Issue: 12(21), P. 4443 - 4443

Published: Oct. 29, 2023

Global warming-induced extreme tropical storms disrupt the operation of offshore wind farms, causing power ramp events and threatening safety interconnected onshore grid. In order to attenuate impact these ramps, this paper proposes an integrated strategy for forecasting controlling ramps in farms. First, characteristics during are studied, a general control framework is established. Second, prediction scheme designed based on minimal gated memory network (MGMN). Third, by taking into account results uncertainties, chance-constraint programming-based optimal developed simultaneously maximize absorption minimize costs. Finally, we use real-world farm data validate effectiveness proposed strategy.

Language: Английский

A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods DOI
Chu Zhang, Yuhan Wang,

Yongyan Fu

et al.

Energy Conversion and Management, Journal Year: 2024, Volume and Issue: 301, P. 118045 - 118045

Published: Jan. 5, 2024

Language: Английский

Citations

23
CL-TGD: A novel point-wise contrastive learning with dynamic temporal granularity data incorporation for wind power prediction DOI
Nanyang Zhu, Ning Jia, Wenjun Bi

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126402 - 126402

Published: Jan. 1, 2025

Language: Английский

Citations

0
A Lightweight Framework for Rapid Response to Short-Term Forecasting of Wind Farms Using Dual Scale Modeling and Normalized Feature Learning DOI Creative Commons
Yan Chen,

Miaolin Yu,

Haochong Wei

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(3), P. 580 - 580

Published: Jan. 26, 2025

Accurate wind power forecasting is crucial for optimizing grid scheduling and improving utilization. However, real-world time series exhibit dynamic statistical properties, such as changing mean variance over time, which make it difficult models to apply observed patterns from the past future. Additionally, execution speed high computational resource demands of complex prediction them deploy on edge computing nodes farms. To address these issues, this paper explores potential linear constructs NFLM, a linear, lightweight, short-term model that more adapted characteristics data. The captures both long-term sequence variations through continuous interval sampling. mitigate interference features, we propose normalization feature learning block (NFLBlock) core component NFLM processing sequences. This module normalizes input data uses stacked multilayer perceptron extract cross-temporal cross-dimensional dependencies. Experiments with two real farms in Guangxi, China, showed compared other advanced methods, MSE 24-step ahead respectively reduced by 23.88% 21.03%, floating-point operations (FLOPs) parameter count only require 36.366 M 0.59 M, respectively. results show can achieve good accuracy fewer resources.

Language: Английский

Citations

0
An effective global structure-aware feature aggregation network for multi-modal medical clustering DOI
Renxiang Guan, Hao Quan, Deliang Li

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 126835 - 126835

Published: Feb. 1, 2025

Language: Английский

Citations

0
A progressive self-supervised learning framework: From fault diagnosis to electric motor wear prediction DOI
Morgane Suhas, Emmanuelle Abisset‐Chavanne, Pierre-André Rey

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127513 - 127513

Published: April 1, 2025

Language: Английский

Citations

0
Semi-supervised contrastive regression for pharmaceutical processes DOI
Yinlong Li, Yilin Liao, Ziyue Sun

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 238, P. 121974 - 121974

Published: Oct. 13, 2023

Language: Английский

Citations

7
Technical indicator enhanced ultra‐short‐term wind power forecasting based on long short‐term memory network combined XGBoost algorithm DOI Creative Commons
Yingying Zheng, Shijie Guan,

Kailei Guo

et al.

IET Renewable Power Generation, Journal Year: 2024, Volume and Issue: unknown

Published: Feb. 23, 2024

Abstract The growing integration of renewable energy sources into the power grid has introduced unprecedented uncertainty. Ensuring an appropriately scheduled reserve is essential to accommodate energy's intermittent and volatile nature. This study introduces innovative approach ultra‐short‐term wind forecasting, which relies on feature engineering a hybrid model. effectiveness this proposed method showcased through case involving utility‐scale farm in Inner Mongolia, China. findings indicate that model, combines XGBoost (Extreme Gradient Boosting) algorithm LSTM (Long Short‐Term Memory) network with KDJ (Stochastic Oscillator), MACD (Moving Average Convergence Divergence), achieves highest forecasting accuracy. Specifically, model yields normalized mean absolute error 0.0396 for forecasting. modelling process takes approximately 550 s. Furthermore, suggested employed predict speed USA. experimental results consistently maintains dependable performance across various raw datasets; it suitable use system operations.

Language: Английский

Citations

2
Hybrid modeling with data enhanced driven learning algorithm for smart generation control in multi-area integrated energy systems with high proportion renewable energy DOI
Linfei Yin, Da Zheng

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 261, P. 125530 - 125530

Published: Oct. 9, 2024

Language: Английский

Citations

2
Data-augmented trend-fluctuation representations by interpretable contrastive learning for wind power forecasting DOI
Yongning Zhao, Haohan Liao, Yuan Zhao

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 380, P. 125052 - 125052

Published: Dec. 12, 2024

Language: Английский

Citations

2
Multi‐Task Residential Short‐Term Load Prediction Based on Contrastive Learning DOI

Wuqing Zhang,

Jianbin Li, Sixing Wu

et al.

IEEJ Transactions on Electrical and Electronic Engineering, Journal Year: 2024, Volume and Issue: 19(5), P. 682 - 689

Published: March 15, 2024

Abstract Load forecasting is crucial for the operation and planning of electricity generation, transmission, distribution. In context short‐term load prediction residential users, single‐task learning methods fail to consider relationship among multiple users have limited feature extraction capabilities data. It challenging obtain sufficient information from individual user predictions, resulting in poor performance. To address these issues, we propose a framework multi‐task based on contrastive learning. Firstly, clustering techniques are used select with similar consumption patterns. Secondly, employed pre‐training, extracting trend seasonal representations sequences, thereby enhancing capability Feature. Lastly, utilized learn shared users' loads, enabling residences. The proposed has been implemented two real‐world data sets, experimental results demonstrate that it effectively reduces errors prediction. © 2024 Institute Electrical Engineer Japan Wiley Periodicals LLC.

Language: Английский

Citations

0