Short-term wind speed prediction based on temporal convolutional networks DOI

Sicheng Fan

2022 IEEE 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Journal Year: 2023, Volume and Issue: unknown, P. 165 - 169

Published: Dec. 8, 2023

To improve the utilization efficiency of wind energy, this research proposes a hybrid model based on Temporal Convolutional Network (TCN) and two-level speed decomposition. Firstly, original data is decomposed into main residual signals through Singular Spectrum Analysis (SSA). Then, usage Variational mode decomposition (VMD) decomposes several sub-sequences. The next step involves predicting signal all sub-sequences using TCN. Eventually, Grey Wolf Optimizer (GWO) employed to perform optimization stack prediction results, resulting in outcomes. results demonstrate that proposed SSA-VMD-TCN-GWO outperforms reference models. Thus, provides new solution

Language: Английский

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review DOI Open Access
Mehrdad Kaveh, Mohammad Saadi Mesgari

Neural Processing Letters, Journal Year: 2022, Volume and Issue: 55(4), P. 4519 - 4622

Published: Oct. 31, 2022

Language: Английский

Citations

131
SCADA system dataset exploration and machine learning based forecast for wind turbines DOI
Upma Singh, M. Rizwan

Results in Engineering, Journal Year: 2022, Volume and Issue: 16, P. 100640 - 100640

Published: Sept. 13, 2022

Language: Английский

Citations

42
A Comprehensive Survey on Aquila Optimizer DOI Open Access
Buddhadev Sasmal, Abdelazim G. Hussien, Arunita Das

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4449 - 4476

Published: June 7, 2023

Language: Английский

Citations

35
Wind speed prediction and reconstruction based on improved grey wolf optimization algorithm and deep learning networks DOI
Anfeng Zhu, Qiancheng Zhao, Tianlong Yang

et al.

Computers & Electrical Engineering, Journal Year: 2024, Volume and Issue: 114, P. 109074 - 109074

Published: Jan. 18, 2024

Language: Английский

Citations

16
A comparison of Holts-Winter and Artificial Neural Network approach in forecasting: A case study for tent manufacturing industry DOI Creative Commons

George Rumbe,

Mohammad M. Hamasha, Sahar ALMashaqbeh

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: 21, P. 101899 - 101899

Published: Feb. 12, 2024

The imperative of accurate forecasting spans diverse industrial sectors, notably impacting the tent manufacturing industry. This study embarks on a rigorous examination and development novel models, specifically tailored for this sector. We introduce juxtapose two distinct approaches: Holt-Winters method Artificial Neural Networks (ANN). Our analysis is grounded in case company, delving into dynamics demand variation, particularly under seasonal influences. Through meticulous comparison, we demonstrate efficacy ANN model, highlighting its superior accuracy forecasting, especially Elite Party Canopy albeit with noted prediction error 15% Vista tents. paper also explores broader supply chain context industry, examining influential factors affecting commercial sales identifying key players. findings underscore nuanced capabilities capturing intricate patterns, offering promising direction refining practices

Language: Английский

Citations

9
Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023 DOI Creative Commons
Dongran Song, Xiao Tan, Qian Huang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(6), P. 1270 - 1270

Published: March 7, 2024

Wind prediction has consistently been in the spotlight as a crucial element achieving efficient wind power generation and reducing operational costs. In recent years, with rapid advancement of artificial intelligence (AI) technology, its application field made significant strides. Focusing on process AI-based modeling, this paper provides comprehensive summary discussion key techniques models data preprocessing, feature extraction, relationship learning, parameter optimization. Building upon this, three major challenges are identified prediction: uncertainty data, incompleteness complexity learning. response to these challenges, targeted suggestions proposed for future research directions, aiming promote effective AI technology address issues therein.

Language: Английский

Citations

6
A soft sensor model based on CNN-BiLSTM and IHHO algorithm for Tennessee Eastman process DOI
Yiman Li, Peng Tian, Wei Sun

et al.

Measurement, Journal Year: 2023, Volume and Issue: 218, P. 113195 - 113195

Published: June 11, 2023

Language: Английский

Citations

14
Electricity demand forecasting based on feature extraction and optimized backpropagation neural network DOI Creative Commons
Eric Ofori-Ntow, Yao Yevenyo Ziggah

e-Prime - Advances in Electrical Engineering Electronics and Energy, Journal Year: 2023, Volume and Issue: 6, P. 100293 - 100293

Published: Sept. 21, 2023

As the global population is growing at a high rate, so electricity demand also increasing faster rate. This exerts pressure on electricity-generating plants and maintenance engineers because of variability in demand. Avoiding disruption supply to meet requires forecasting what future will look like be able plan adequately towards it. study, therefore, develops new model using feature extraction (FE) where statistical information hourly data extracted which serves as input variables for Backpropagation neural network (BPNN) optimized by particle swarm optimization (PSO) Ghana. The known FE-PSO-BPNN compared other seven models such Radial Basis Function (RBFNN), Random Forest (RF), Gradient Boosting Machine (GBM), Multivariate Adaptive Regression Splines (MARS), BPNN, PSO-RBFNN FE selects all models. Electricity from Ghana Grid Company period including 1st September 2018 30th November 2019 used testing model's performance. Evaluation criteria Root Mean Square Error (RMSE), Absolute (MAE), Percentage (MAPE), Scatter Index (SI) were used. proposed more powerful than others it has RMSE (0.5344), MAE (3.3845), MAPE (0.1773), SI (0.0003). expected better option sector managers when considering forecasting.

Language: Английский

Citations

11
Enhanced Streamflow Forecasting for Crisis Management Based on Hybrid Extreme Gradient Boosting Model DOI
Hamed Khajavi, Amir Rastgoo, Fariborz Masoumi

et al.

Iranian Journal of Science and Technology Transactions of Civil Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 13, 2025

Language: Английский

Citations

0
Short‐Term Wind Power Prediction Based on MVMD‐AVOA‐CNN‐LSTM‐AM DOI Creative Commons
Xiqing Zang,

Zehua Wang,

S. W. Zhang

et al.

International Transactions on Electrical Energy Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

Due to the intermittent and fluctuating nature of wind power generation, it is difficult achieve desired prediction accuracy for prediction. For this reason, paper proposes a combined model based on Pearson correlation coefficient method, multivariate variational mode decomposition (MVMD), African vultures optimization algorithm (AVOA) leader–follower patterns, convolutional neural network (CNN), long short‐term memory (LSTM), attention mechanism (AM). Firstly, method used filter out meteorological data with strong relationship establish dataset; subsequently, MVMD decompose original into multiple subsequences in order handle better. Thereafter, optimize hyperparameters CNN‐LSTM algorithm, AM added increase effect, decomposed are predicted separately, values each subsequence superimposed obtain final value. Finally, effectiveness verified using from farm Shenyang. The results show that MAE established MVMD‐AVA‐CNN‐LSTM‐AM 2.0467, MSE 2.8329. Compared other models, significantly improved, had better generalization ability robustness, robustness.

Language: Английский

Citations

0