BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Li C, Lin S, Xu F, Liu D, Liu J. Short-term wind power prediction based on data mining technology and improved support vector machine method: A case study in Northwest China. Journal of Cleaner Production 2018;205:909-22. [DOI: 10.1016/j.jclepro.2018.09.143] [Cited by in Crossref: 60] [Cited by in F6Publishing: 13] [Article Influence: 15.0] [Reference Citation Analysis]
Number Citing Articles
1 Dong F, Shi L. Regional differences study of renewable energy performance: A case of wind power in China. Journal of Cleaner Production 2019;233:490-500. [DOI: 10.1016/j.jclepro.2019.06.098] [Cited by in Crossref: 27] [Cited by in F6Publishing: 1] [Article Influence: 9.0] [Reference Citation Analysis]
2 Liu H, Chen C. Data processing strategies in wind energy forecasting models and applications: A comprehensive review. Applied Energy 2019;249:392-408. [DOI: 10.1016/j.apenergy.2019.04.188] [Cited by in Crossref: 68] [Cited by in F6Publishing: 3] [Article Influence: 22.7] [Reference Citation Analysis]
3 Wang Y, Yu Y, Cao S, Zhang X, Gao S. A review of applications of artificial intelligent algorithms in wind farms. Artif Intell Rev 2020;53:3447-500. [DOI: 10.1007/s10462-019-09768-7] [Cited by in Crossref: 31] [Cited by in F6Publishing: 1] [Article Influence: 10.3] [Reference Citation Analysis]
4 Ofori-ntow Jnr E, Ziggah YY, Rodrigues MJ, Relvas S. A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction. Results in Engineering 2022. [DOI: 10.1016/j.rineng.2022.100399] [Reference Citation Analysis]
5 Demolli H, Dokuz AS, Ecemis A, Gokcek M. Wind power forecasting based on daily wind speed data using machine learning algorithms. Energy Conversion and Management 2019;198:111823. [DOI: 10.1016/j.enconman.2019.111823] [Cited by in Crossref: 81] [Article Influence: 27.0] [Reference Citation Analysis]
6 Chen C, Liu H. Medium-term wind power forecasting based on multi-resolution multi-learner ensemble and adaptive model selection. Energy Conversion and Management 2020;206:112492. [DOI: 10.1016/j.enconman.2020.112492] [Cited by in Crossref: 20] [Cited by in F6Publishing: 6] [Article Influence: 10.0] [Reference Citation Analysis]
7 Naseri H, Jahanbakhsh H, Hosseini P, Moghadas Nejad F. Designing sustainable concrete mixture by developing a new machine learning technique. Journal of Cleaner Production 2020;258:120578. [DOI: 10.1016/j.jclepro.2020.120578] [Cited by in Crossref: 16] [Cited by in F6Publishing: 2] [Article Influence: 8.0] [Reference Citation Analysis]
8 Kisvari A, Lin Z, Liu X. Wind power forecasting – A data-driven method along with gated recurrent neural network. Renewable Energy 2021;163:1895-909. [DOI: 10.1016/j.renene.2020.10.119] [Cited by in Crossref: 31] [Cited by in F6Publishing: 9] [Article Influence: 31.0] [Reference Citation Analysis]
9 Tian Z. A state-of-the-art review on wind power deterministic prediction. Wind Engineering 2021;45:1374-92. [DOI: 10.1177/0309524x20941203] [Cited by in Crossref: 4] [Article Influence: 2.0] [Reference Citation Analysis]
10 Zhang Y, Han J, Pan G, Xu Y, Wang F. A multi-stage predicting methodology based on data decomposition and error correction for ultra-short-term wind energy prediction. Journal of Cleaner Production 2021;292:125981. [DOI: 10.1016/j.jclepro.2021.125981] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 8.0] [Reference Citation Analysis]
11 Hu H, Hu Z, Zhong K, Xu J, Wu P, Zhao Y, Zhang F. Long-term offshore wind power prediction using spatiotemporal kriging: A case study in China’s Guangdong Province. Energy Exploration & Exploitation 2020;38:703-22. [DOI: 10.1177/0144598719889368] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
12 Cheng Q, Chen Y, Xiao Y, Yin H, Liu W. A dual-stage attention-based Bi-LSTM network for multivariate time series prediction. J Supercomput. [DOI: 10.1007/s11227-022-04506-3] [Reference Citation Analysis]
13 Shams MH, Niaz H, Hashemi B, Jay Liu J, Siano P, Anvari-moghaddam A. Artificial intelligence-based prediction and analysis of the oversupply of wind and solar energy in power systems. Energy Conversion and Management 2021;250:114892. [DOI: 10.1016/j.enconman.2021.114892] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Pourghasemi HR, Yousefi S, Sadhasivam N, Eskandari S. Assessing, mapping, and optimizing the locations of sediment control check dams construction. Sci Total Environ 2020;739:139954. [PMID: 32544688 DOI: 10.1016/j.scitotenv.2020.139954] [Cited by in Crossref: 5] [Cited by in F6Publishing: 1] [Article Influence: 2.5] [Reference Citation Analysis]
15 Zhang Y, Chen Y. Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction. Environ Sci Pollut Res Int 2021. [PMID: 34797536 DOI: 10.1007/s11356-021-16997-3] [Reference Citation Analysis]
16 Liu Z, Li L, Tseng M, Lim MK. Prediction short-term photovoltaic power using improved chicken swarm optimizer - Extreme learning machine model. Journal of Cleaner Production 2020;248:119272. [DOI: 10.1016/j.jclepro.2019.119272] [Cited by in Crossref: 33] [Cited by in F6Publishing: 3] [Article Influence: 16.5] [Reference Citation Analysis]
17 Gao B. The Use of Machine Learning Combined with Data Mining Technology in Financial Risk Prevention. Comput Econ. [DOI: 10.1007/s10614-021-10101-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Li L, Chang Y, Tseng M, Liu J, Lim MK. Wind power prediction using a novel model on wavelet decomposition-support vector machines-improved atomic search algorithm. Journal of Cleaner Production 2020;270:121817. [DOI: 10.1016/j.jclepro.2020.121817] [Cited by in Crossref: 14] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
19 Liu M, Cao Z, Zhang J, Wang L, Huang C, Luo X. Short-term wind speed forecasting based on the Jaya-SVM model. International Journal of Electrical Power & Energy Systems 2020;121:106056. [DOI: 10.1016/j.ijepes.2020.106056] [Cited by in Crossref: 36] [Cited by in F6Publishing: 1] [Article Influence: 18.0] [Reference Citation Analysis]
20 Gong M, Wang J, Bai Y, Li B, Zhang L. Heat load prediction of residential buildings based on discrete wavelet transform and tree-based ensemble learning. Journal of Building Engineering 2020;32:101455. [DOI: 10.1016/j.jobe.2020.101455] [Cited by in Crossref: 10] [Cited by in F6Publishing: 1] [Article Influence: 5.0] [Reference Citation Analysis]
21 Kan A, Zeng Y, Meng X, Wang D, Xina J, Yang X, Tesren L. The linkage between renewable energy potential and sustainable development: Understanding solar energy variability and photovoltaic power potential in Tibet, China. Sustainable Energy Technologies and Assessments 2021;48:101551. [DOI: 10.1016/j.seta.2021.101551] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Yang W, Wang J, Lu H, Niu T, Du P. Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China. Journal of Cleaner Production 2019;222:942-59. [DOI: 10.1016/j.jclepro.2019.03.036] [Cited by in Crossref: 62] [Cited by in F6Publishing: 14] [Article Influence: 20.7] [Reference Citation Analysis]
23 An Y, Wang J, Lu H, Zhao W. Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm. Int Trans Electr Energ Syst 2021;31. [DOI: 10.1002/2050-7038.13189] [Reference Citation Analysis]
24 Liu H, Chen C, Lv X, Wu X, Liu M. Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management 2019;195:328-45. [DOI: 10.1016/j.enconman.2019.05.020] [Cited by in Crossref: 64] [Cited by in F6Publishing: 7] [Article Influence: 21.3] [Reference Citation Analysis]
25 Xue Y, Ren J, Bi X. Impact of Influencing Factors on CO2 Emissions in the Yangtze River Delta during Urbanization. Sustainability 2019;11:4183. [DOI: 10.3390/su11154183] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Tian Z. Approach for short-term wind power prediction via kernel principal component analysis and echo state network optimized by improved particle swarm optimization algorithm. Transactions of the Institute of Measurement and Control 2021;43:3647-62. [DOI: 10.1177/01423312211046421] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Kabilan R, Chandran V, Yogapriya J, Karthick A, Gandhi PP, Mohanavel V, Rahim R, Manoharan S, Hupkes J. Short-Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms. International Journal of Photoenergy 2021;2021:1-11. [DOI: 10.1155/2021/5582418] [Cited by in Crossref: 8] [Cited by in F6Publishing: 3] [Article Influence: 8.0] [Reference Citation Analysis]
28 Hu H, Wang L, Lv S. Forecasting energy consumption and wind power generation using deep echo state network. Renewable Energy 2020;154:598-613. [DOI: 10.1016/j.renene.2020.03.042] [Cited by in Crossref: 45] [Cited by in F6Publishing: 9] [Article Influence: 22.5] [Reference Citation Analysis]
29 Yang, Zhang, Yang, Lv. Deterministic and Probabilistic Wind Power Forecasting Based on Bi-Level Convolutional Neural Network and Particle Swarm Optimization. Applied Sciences 2019;9:1794. [DOI: 10.3390/app9091794] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
30 Fu C, Li G, Lin K, Zhang H. Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine. Sustainability 2019;11:512. [DOI: 10.3390/su11020512] [Cited by in Crossref: 20] [Cited by in F6Publishing: 3] [Article Influence: 6.7] [Reference Citation Analysis]
31 Zhang Y, Pan G. A hybrid prediction model for forecasting wind energy resources. Environ Sci Pollut Res 2020;27:19428-46. [DOI: 10.1007/s11356-020-08452-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
32 Jiajun H, Chuanjin Y, Yongle L, Huoyue X. Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning. Energy Conversion and Management 2020;205:112418. [DOI: 10.1016/j.enconman.2019.112418] [Cited by in Crossref: 27] [Cited by in F6Publishing: 3] [Article Influence: 13.5] [Reference Citation Analysis]
33 Harrou F, Saidi A, Sun Y. Wind power prediction using bootstrap aggregating trees approach to enabling sustainable wind power integration in a smart grid. Energy Conversion and Management 2019;201:112077. [DOI: 10.1016/j.enconman.2019.112077] [Cited by in Crossref: 12] [Cited by in F6Publishing: 1] [Article Influence: 4.0] [Reference Citation Analysis]
34 Bo G, Keke L, Hongtao Z, Jinhua Z, Hui H. Short-Term Forecasting and Uncertainty Analysis of Wind Power. Journal of Solar Energy Engineering 2021;143:054503. [DOI: 10.1115/1.4050594] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]