A novel multi-step interval prediction model for uncertain data using iterative lower upper bound estimation method DOI
Chongyang Xu, Yubin Wang, Shouping Guan

et al.

International Journal of Approximate Reasoning, Journal Year: 2025, Volume and Issue: unknown, P. 109451 - 109451

Published: April 1, 2025

Language: Английский

Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm DOI
Jianzhou Wang, Mengzheng Lv, Zhiwu Li

et al.

Expert Systems with Applications, Journal Year: 2022, Volume and Issue: 214, P. 119129 - 119129

Published: Nov. 1, 2022

Language: Английский

Citations

63
A hybrid multi-criteria decision-making framework for offshore wind turbine selection: A case study in China DOI
Yang Yu, Shibo Wu, Jianxing Yu

et al.

Applied Energy, Journal Year: 2022, Volume and Issue: 328, P. 120173 - 120173

Published: Oct. 28, 2022

Language: Английский

Citations

44
A novel wind power forecasting system integrating time series refining, nonlinear multi-objective optimized deep learning and linear error correction DOI Open Access
Jianzhou Wang, Yuansheng Qian,

Linyue Zhang

et al.

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 299, P. 117818 - 117818

Published: Nov. 16, 2023

Language: Английский

Citations

42
A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network DOI
Huifeng Xu, Feihu Hu, Xinhao Liang

et al.

Energy, Journal Year: 2024, Volume and Issue: 299, P. 131258 - 131258

Published: April 25, 2024

Language: Английский

Citations

14
Carbon emission price point-interval forecasting based on multivariate variational mode decomposition and attention-LSTM model DOI
Liling Zeng, Huanling Hu, Huajun Tang

et al.

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 157, P. 111543 - 111543

Published: March 29, 2024

Language: Английский

Citations

13
Minute-level ultra-short-term power load forecasting based on time series data features DOI Creative Commons
Chuang Wang,

Haishen Zhao,

Yang Liu

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 372, P. 123801 - 123801

Published: July 3, 2024

Electricity is fundamental to the development of national economies and societies, reliant on accurate power load forecasting for its stable supply. Ultra-short-term analyzes historical data predict changes within next hour. This crucial achieving efficient dispatching, improving emergency management, ensuring operation system. However, with increasingly widespread application renewable energy, inherent intermittency exacerbates complexity randomness loads, posing a challenge models accurately capture features. In addressing this challenge, study presents novel method feature extraction from time series data, aimed at enhancing accuracy forecasting. By analyzing trend, periodicities, randomness, it simplifies complex into several features, significantly reducing noise-induced errors identification understanding Moreover, applies five prevalent deep learning models. Experimental results show that using reduces mean absolute percentage error by an average 54.6905%, 42.6654%, 51.3868% datasets three different substations in China. These not only affirm method's efficacy but also provide new technical foundations reliable functioning future systems.

Language: Английский

Citations

9
Multivariate rolling decomposition hybrid learning paradigm for power load forecasting DOI
Aiting Xu, Jinrun Chen, Jinchang Li

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 212, P. 115375 - 115375

Published: Jan. 23, 2025

Language: Английский

Citations

1
A coupled framework for power load forecasting with Gaussian implicit spatio temporal block and attention mechanisms network DOI
Dezhi Liu, Xuan Lin,

Hanyang Liu

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110263 - 110263

Published: March 20, 2025

Language: Английский

Citations

1
A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting DOI
Jianzhou Wang, Kang Wang, Zhiwu Li

et al.

IEEE Transactions on Power Systems, Journal Year: 2023, Volume and Issue: 39(1), P. 1240 - 1250

Published: March 15, 2023

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency shedding devices. Such a predicament poses grave threat the economic and social welfare affected region, in extreme scenarios, result debilitating grid collapse. However, existing models lack deep feature extraction capability cannot accurately predict uncertainty electricity demand. Thus, multitask integrated deep-learning probabilistic prediction with multidimensional based on granularity information quantile regression is explored this study solve insufficient problem prediction. In proposed scheme, we designed multi-layer framework, which fuzzy rough sets extract time-domain features, multilayer Laplace operators features globally, recurrent neural network variants time context information. addition, Pareto integration method extended capture better individuals. The experimental results demonstrate that system effectively improve deterministic analysis demand manage daily fluctuations.

Language: Английский

Citations

20
A novel multivariate combined power load forecasting system based on feature selection and multi-objective intelligent optimization DOI
Qianyi Xing,

Xiaojia Huang,

Jianzhou Wang

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 244, P. 122970 - 122970

Published: Dec. 20, 2023

Language: Английский

Citations

18