BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Yildiz C, Acikgoz H, Korkmaz D, Budak U. An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Conversion and Management 2021;228:113731. [DOI: 10.1016/j.enconman.2020.113731] [Cited by in Crossref: 17] [Cited by in F6Publishing: 1] [Article Influence: 17.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang J, Zhang L, Wang C, Liu Z. A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting. Applied Soft Computing 2021;113:107941. [DOI: 10.1016/j.asoc.2021.107941] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
2 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]
3 Malhan P, Mittal M. A novel ensemble model for long-term forecasting of wind and hydro power generation. Energy Conversion and Management 2022;251:114983. [DOI: 10.1016/j.enconman.2021.114983] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
4 Ciaburro G, Iannace G. Machine Learning-Based Algorithms to Knowledge Extraction from Time Series Data: A Review. Data 2021;6:55. [DOI: 10.3390/data6060055] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
5 Chen X, Gao W, Hong C, Tu Y. A novel series arc fault detection method for photovoltaic system based on multi-input neural network. International Journal of Electrical Power & Energy Systems 2022;140:108018. [DOI: 10.1016/j.ijepes.2022.108018] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Chen H, Birkelund Y, Zhang Q. Data-augmented sequential deep learning for wind power forecasting. Energy Conversion and Management 2021;248:114790. [DOI: 10.1016/j.enconman.2021.114790] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
7 Piotrowski P, Baczyński D, Kopyt M, Gulczyński T. Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms. Energies 2022;15:1252. [DOI: 10.3390/en15041252] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Eroğlu Y, Yildirim M, Çinar A. Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR. Comput Biol Med 2021;133:104407. [PMID: 33901712 DOI: 10.1016/j.compbiomed.2021.104407] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
9 Lipu MSH, Miah MS, Hannan MA, Hussain A, Sarker MR, Ayob A, Saad MHM, Mahmud MS. Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects. IEEE Access 2021;9:102460-89. [DOI: 10.1109/access.2021.3097102] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
10 Acikgoz H, Budak U, Korkmaz D, Yildiz C. WSFNet: An efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network. Energy 2021;233:121121. [DOI: 10.1016/j.energy.2021.121121] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
11 Zhao L, Nazir MS, Nazir HMJ, Abdalla AN. A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management. Environ Sci Pollut Res Int 2022;29:43690-709. [PMID: 35435552 DOI: 10.1007/s11356-022-19902-8] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Niu H, Yang Y, Zeng L, Li Y. ELM-QR-Based Nonparametric Probabilistic Prediction Method for Wind Power. Energies 2021;14:701. [DOI: 10.3390/en14030701] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
13 Singh U, Rizwan M, Malik H, García Márquez FP. Wind Energy Scenario, Success and Initiatives towards Renewable Energy in India—A Review. Energies 2022;15:2291. [DOI: 10.3390/en15062291] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
14 Wu Z, Luo G, Yang Z, Guo Y, Li K, Xue Y. A comprehensive review on deep learning approaches in wind forecasting applications. CAAI Trans on Intel Tech. [DOI: 10.1049/cit2.12076] [Reference Citation Analysis]
15 Xu H, Chang Y, Wang F, Wang S, Yao Y. Univariate and multivariable forecasting models for ultra-short-term wind power prediction based on the similar day and LSTM network. Journal of Renewable and Sustainable Energy 2021;13:063307. [DOI: 10.1063/5.0027130] [Reference Citation Analysis]
16 Al-qaness MA, Ewees AA, Fan H, Abualigah L, Elaziz MA. Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting. Applied Energy 2022;314:118851. [DOI: 10.1016/j.apenergy.2022.118851] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
17 Heydari A, Majidi Nezhad M, Neshat M, Garcia DA, Keynia F, De Santoli L, Bertling Tjernberg L. A Combined Fuzzy GMDH Neural Network and Grey Wolf Optimization Application for Wind Turbine Power Production Forecasting Considering SCADA Data. Energies 2021;14:3459. [DOI: 10.3390/en14123459] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
18 Lv S, Wang L. Deep learning combined wind speed forecasting with hybrid time series decomposition and multi-objective parameter optimization. Applied Energy 2022;311:118674. [DOI: 10.1016/j.apenergy.2022.118674] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 5.0] [Reference Citation Analysis]
19 Niu D, Sun L, Yu M, Wang K. Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model. Energy 2022. [DOI: 10.1016/j.energy.2022.124384] [Reference Citation Analysis]
20 Alkhayat G, Mehmood R. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI 2021;4:100060. [DOI: 10.1016/j.egyai.2021.100060] [Cited by in Crossref: 15] [Cited by in F6Publishing: 1] [Article Influence: 15.0] [Reference Citation Analysis]
21 Meng A, Chen S, Ou Z, Ding W, Zhou H, Fan J, Yin H. A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization. Energy 2022;238:121795. [DOI: 10.1016/j.energy.2021.121795] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
22 Xiong B, Lou L, Meng X, Wang X, Ma H, Wang Z. Short-term wind power forecasting based on Attention Mechanism and Deep Learning. Electric Power Systems Research 2022;206:107776. [DOI: 10.1016/j.epsr.2022.107776] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
23 Acikgoz H. A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting. Applied Energy 2022;305:117912. [DOI: 10.1016/j.apenergy.2021.117912] [Cited by in Crossref: 7] [Cited by in F6Publishing: 1] [Article Influence: 7.0] [Reference Citation Analysis]
24 Qiao D, Wu S, Li G, You J, Zhang J, Shen B. Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys. Renewable Energy 2022;189:231-44. [DOI: 10.1016/j.renene.2022.02.095] [Reference Citation Analysis]
25 Li H, He Y, Xu Q, Deng J, Li W, Wei Y. Detection and segmentation of loess landslides via satellite images: a two-phase framework. Landslides. [DOI: 10.1007/s10346-021-01789-0] [Cited by in Crossref: 22] [Cited by in F6Publishing: 16] [Article Influence: 22.0] [Reference Citation Analysis]
26 Korkmaz D. SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting. Applied Energy 2021;300:117410. [DOI: 10.1016/j.apenergy.2021.117410] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 11.0] [Reference Citation Analysis]