Approach for Short-Term Power Load Prediction Utilizing the ICEEMDAN–LSTM–TCN–Bagging Model DOI

Guo-Qiang Zheng,

Lingrui Kong,

Zhonge Su

et al.

Journal of Electrical Engineering and Technology, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 24, 2024

Language: Английский

CNNs-Transformer based day-ahead probabilistic load forecasting for weekends with limited data availability DOI
Zhirui Tian, Weican Liu, Wenqian Jiang

et al.

Energy, Journal Year: 2024, Volume and Issue: 293, P. 130666 - 130666

Published: Feb. 10, 2024

Language: Английский

Citations

34
Forecasting hourly PM2.5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms DOI Creative Commons

Peilei Cai,

Chengyuan Zhang, Jian Chai

et al.

Data Science and Management, Journal Year: 2023, Volume and Issue: 6(1), P. 46 - 54

Published: March 1, 2023

Language: Английский

Citations

29
An improved Wavenet network for multi-step-ahead wind energy forecasting DOI
Yun Wang, Tuo Chen, Shengchao Zhou

et al.

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 278, P. 116709 - 116709

Published: Feb. 1, 2023

Language: Английский

Citations

26
Novel optimization approach for realized volatility forecast of stock price index based on deep reinforcement learning model DOI Open Access
Yuanyuan Yu, Yu Lin,

Xianping Hou

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 233, P. 120880 - 120880

Published: June 22, 2023

Language: Английский

Citations

26
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
Deterministic and probabilistic wind speed forecasting using decomposition methods: Accuracy and uncertainty DOI
Qian Sun, Jinxing Che, Kun Hu

et al.

Renewable Energy, Journal Year: 2025, Volume and Issue: unknown, P. 122515 - 122515

Published: Jan. 1, 2025

Language: Английский

Citations

1
FCDT-IWBOA-LSSVR: An innovative hybrid machine learning approach for efficient prediction of short-to-mid-term photovoltaic generation DOI
Liang Lu,

Tiecheng Su,

Yuxiang Gao

et al.

Journal of Cleaner Production, Journal Year: 2022, Volume and Issue: 385, P. 135716 - 135716

Published: Dec. 23, 2022

Language: Английский

Citations

32
Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction DOI Creative Commons
Nier Wang, Zhanming Li

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 6, 2023

Aiming at the strong non-linear and non-stationary characteristics of power load, a short-term load forecasting method based on bald eagle search (BES) optimization variational mode decomposition (VMD), convolutional bi-directional long memory (CNN-Bi-LSTM) network considering error correction is studied to improve accuracy forecasting. Firstly, loss evaluation criterion established, VMD optimal parameters under are determined BES quality signal. Then, original sequence decomposed into different modal components, corresponding CNN-Bi-LSTM prediction models established for each component. In addition, influence various holiday meteorological factors error, an model mine hidden information contained in reduce inherent model. Finally, proposed applied public dataset provided by utility United States. The results show that this can better track changes effectively

Language: Английский

Citations

21
Multi-step Carbon Emissions Forecasting Model for Industrial Process Based on a New Strategy and Machine Learning Methods DOI

Yusha Hu,

Yi Man, Jingzheng Ren

et al.

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: 187, P. 1213 - 1233

Published: May 14, 2024

Language: Английский

Citations

6
Multistep power load forecasting using iterative neural network-based prediction intervals DOI
Shouping Guan, Chongyang Xu, Tianyi Guan

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 119, P. 109518 - 109518

Published: Aug. 7, 2024

Language: Английский

Citations

5