Wu, Z. et al. Effect of dielectric leisure of epoxy resin on dielectric lack of medium-frequency transformer. IEEE Trans. Dielectr. Electr. Insul. 29, 1651–1658 (2022).
Google Scholar
Lu, S. et al. Adaptive management of time delay teleoperation system with unsure dynamics. Front. Neurorobot. 16, 928863. https://doi.org/10.3389/fnbot.2022.928863 (2022).Article
PubMed
PubMed Central
Google Scholar
Lu, S. et al. Multiscale function extraction and fusion of picture and textual content in VQA. Int. J. Comput. Intell. Syst. 16, 54. https://doi.org/10.1007/s44196-023-00233-6 (2023).Article
Google Scholar
Lu, S. et al. The multi-modal fusion in visible query answering: a overview of consideration mechanisms. PeerJ Comput. Sci. 9, e1400. https://doi.org/10.7717/peerj-cs.1400 (2023).Article
ADS
PubMed
PubMed Central
Google Scholar
Zheng, H. et al. A multi-scale point-supervised network for counting maize tassels within the wild. Plant Phenomics (2023).Cheng, B. et al. Situation-aware dynamic service coordination in an IoT surroundings. IEEE/ACM Trans. Netw. 25, 2082–2095. https://doi.org/10.1109/TNET.2017.2705239 (2017).Article
Google Scholar
Yang, X., Wang, X., Wang, S. & Puig, V. Switching-based adaptive fault-tolerant management for unsure nonlinear programs towards actuator and sensor faults. J. Franklin Inst. 360, 11462–11488 (2023).MathSciInternet
Google Scholar
Jiang, J., Zhang, L., Wen, X., Valipour, E. & Nojavan, S. Risk-based efficiency of power-to-gas storage expertise built-in with vitality hub system relating to draw back danger constrained strategy. Int. J. Hydrogen Energy 47, 39429–39442 (2022).CAS
Google Scholar
Li, S. & Liu, Z. Scheduling uniform machines with restricted task. Math. Biosci. Eng 19, 9697–9708 (2022).MathSciInternet
PubMed
Google Scholar
Zhang, J. et al. Enhanced effectivity with CDCA co-adsorption for dye-sensitized solar cells based on metallosalophen complexes. Solar Energy 209, 316–324 (2020).ADS
CAS
Google Scholar
Huang, S., Huang, M. & Lyu, Y. Seismic efficiency evaluation of a wind turbine with a monopile basis affected by sea ice based on a easy numerical technique. Eng. Appl. Comput. Fluid Mech. 15, 1113–1133 (2021).
Google Scholar
Chen, H., Wu, H., Kan, T., Zhang, J. & Li, H. Low-carbon financial dispatch of built-in vitality system containing electrical hydrogen manufacturing based on VMD-GRU short-term wind energy prediction. Int. J. Electrical Power Energy Syst. 154, 109420 (2023).
Google Scholar
Zhu, D. et al. Feedforward Frequency Deviation Control in PLL for Fast Inertial Response of DFIG-Based Wind Turbines. IEEE Trans. Power Electronics (2023).Gong, J., Li, C. & Wasielewski, M. R. Advances in solar vitality conversion. Chem. Soc. Rev. 48, 1862–1864. https://doi.org/10.1039/C9CS90020A (2019).Article
CAS
PubMed
Google Scholar
Zhang, W., Zheng, Z. & Liu, H. Droop management technique to realize most energy output of photovoltaic for parallel inverter system. CSEE J. Power Energy Syst. 8, 1636–1645 (2021).
Google Scholar
Blal, M. et al. A prediction fashions for estimating international solar radiation and analysis meteorological impact on solar radiation potential underneath a number of climate circumstances on the floor of Adrar surroundings. Measurement 152, 107348. https://doi.org/10.1016/j.measurement.2019.107348 (2020).Article
Google Scholar
Chakchak, J. & Cetin, N. S. Investigating the influence of climate parameters choice on the prediction of solar radiation underneath totally different genera of cloud cowl: A case-study in a subtropical location. Measurement 176, 109159 (2021).
Google Scholar
Mishra, M., Dash, P. B., Nayak, J., Naik, B. & Swain, S. Ok. Deep learning and wavelet rework built-in strategy for short-term solar PV energy prediction. Measurement 166, 108250 (2020).
Google Scholar
Patel, S. Ok. et al. Ultra‐Wideband, Polarization‐Independent, Wide‐Angle Multilayer Swastika‐Shaped Metamaterial Solar Energy Absorber with Absorption Prediction utilizing Machine Learning. Adv. Theory Simul., 2100604 (2022).Zazoum, B. Solar photovoltaic energy prediction utilizing totally different machine learning strategies. Energy Rep. 8, 19–25 (2022).
Google Scholar
Patel, D., Patel, S., Patel, P. & Shah, M. Solar radiation and solar vitality estimation utilizing ANN and Fuzzy logic idea: A complete and systematic research. Environ. Sci. Pollut. Res., 1–15 (2022).Heng, S. Y. et al. Artificial neural network mannequin with totally different backpropagation algorithms and meteorological knowledge for solar radiation prediction. Sci. Rep. 12, 1–18 (2022).ADS
Google Scholar
Ghimire, S., Nguyen-Huy, T., Deo, R. C., Casillas-Perez, D. & Salcedo-Sanz, S. Efficient each day solar radiation prediction with deep learning 4-phase convolutional neural network, twin stage stacked regression and assist vector machine CNN-REGST hybrid mannequin. Sustain. Mater. Technol. 32, e00429 (2022).
Google Scholar
Guermoui, M., Abdelaziz, R., Gairaa, Ok., Djemoui, L. & Benkaciali, S. New temperature-based predicting mannequin for international solar radiation utilizing assist vector regression. Int. J. Ambient Energy 43, 1397–1407 (2022).
Google Scholar
Singh, N., Jena, S. & Panigrahi, C. Ok. A novel software of resolution tree classifier in solar irradiance prediction. Mater. Today: Proc. 58, 316–323 (2022).
Google Scholar
Shorabeh, S. N. et al. A call mannequin based on resolution tree and particle swarm optimization algorithms to determine optimum areas for solar energy crops building in Iran. Renew. Energy 187, 56–67 (2022).
Google Scholar
Guerra, M. I., de Araújo, F. M., de Carvalho Neto, J. T. & Vieira, R. G. Survey on adaptative neural fuzzy inference system (ANFIS) structure utilized to photovoltaic programs. Energy Syst., 1–37 (2022).Fraihat, H. et al. Solar radiation forecasting by pearson correlation utilizing LSTM neural network and ANFIS technique: software within the west-central Jordan. Future Internet 14, 79 (2022).
Google Scholar
Moayedi, H. et al. Novel hybrids of adaptive neuro-fuzzy inference system (ANFIS) with a number of metaheuristic algorithms for spatial susceptibility evaluation of seismic-induced landslide. Geomatics, Natl. Hazards Risk 10, 1879–1911. https://doi.org/10.1080/19475705.2019.1650126 (2019).Article
Google Scholar
Jahanafroozi, N. et al. New heuristic strategies for sustainable vitality efficiency evaluation of HVAC programs. Sustainability 14, 14446 (2022).
Google Scholar
Liu, L., Zhang, S., Zhang, L., Pan, G. & Yu, J. Multi-UUV maneuvering counter-game for dynamic goal situation based on fractional-order recurrent neural network. IEEE Trans. Cybern. (2022).Luo, J., Wang, G., Li, G. & Pesce, G. Transport infrastructure connectivity and battle decision: a machine learning evaluation. Neural Comput. Appl. 34, 6585–6601 (2022).
Google Scholar
Cheng, Y. et al. A dual-branch weakly supervised learning based network for correct mapping of woody vegetation from distant sensing photos. Int. J. Appl. Earth Obs. Geoinf. 124, 103499 (2023).
Google Scholar
Kim, J.-H., Seong, N.-C. & Choi, W. Forecasting the Energy Consumption of an Actual Air Handling Unit and Absorption Chiller Using ANN Models. Energies 13, 4361 (2020).
Google Scholar
Bhatt, G. A. & Gandhi, P. R. in third International Conference on Trends in Electronics and Informatics (ICOEI). 622–627.Yin, Z., Jia, B., Wu, S., Dai, J. & Tang, D. Comprehensive forecast of city water-energy demand based on a neural network mannequin. Water 10, 385 (2018).
Google Scholar
Malvoni, M., De Giorgi, M. G. & Congedo, P. M. Data on assist vector machines (SVM) mannequin to forecast photovoltaic energy. Data Brief 9, 13–16. https://doi.org/10.1016/j.dib.2016.08.024 (2016).Article
CAS
PubMed
PubMed Central
Google Scholar
Tian, H., Li, R., Salah, B. & Thinh, P.-H. Bi-objective optimization and environmental evaluation of SOFC-based cogeneration system: Performance analysis with numerous natural fluids. Process Saf. Environ. Prot. 178, 311–330 (2023).CAS
Google Scholar
Li, R., Xu, D., Tian, H. & Zhu, Y. Multi-objective research and optimization of a solar-boosted geothermal flash cycle built-in into an innovative mixed energy and desalinated water manufacturing course of: Application of a case research. Energy 282, 128706 (2023).
Google Scholar
Zhang, Z., Altalbawy, F. M., Al-Bahrani, M. & Riadi, Y. Regret-based multi-objective optimization of carbon seize facility in CHP-based microgrid with carbon dioxide biking. J. Clean. Prod. 384, 135632 (2023).CAS
Google Scholar
Afzal, A. et al. Optimizing the thermal efficiency of solar vitality units utilizing meta-heuristic algorithms: A vital overview. Renew. Sustain. Energy Rev. 173, 112903 (2023).
Google Scholar
Stoean, C. et al. Metaheuristic-based hyperparameter tuning for recurrent deep learning: Application to the prediction of solar vitality technology. Axioms 12, 266. https://doi.org/10.3390/axioms12030266 (2023).Article
Google Scholar
Alkhazaleh, H. A. et al. Prediction of thermal vitality demand utilizing fuzzy-based fashions synthesized with metaheuristic algorithms. Sustainability 14, 14385 (2022).
Google Scholar
Houssein, E. H. in Advanced Control and Optimization Paradigms for Wind Energy Systems 165–187 (Springer, 2019).Corizzo, R., Ceci, M., Fanaee-T, H. & Gama, J. Multi-aspect renewable vitality forecasting. Inf. Sci. 546, 701–722. https://doi.org/10.1016/j.ins.2020.08.003 (2021).Article
MathSciInternet
Google Scholar
Bessa, R. J., Trindade, A., Silva, C. S. P. & Miranda, V. Probabilistic solar energy forecasting in sensible grids utilizing distributed info. Int. J. Electrical Power Energy Syst. 72, 16–23. https://doi.org/10.1016/j.ijepes.2015.02.006 (2015).Article
Google Scholar
Liu, H., Chen, C., Lv, X., Wu, X. & Liu, M. Deterministic wind vitality forecasting: A overview of clever predictors and auxiliary strategies. Energy Convers. Manag. 195, 328–345 (2019).
Google Scholar
Cavalcante, L., Bessa, R. J., Reis, M. & Browell, J. LASSO vector autoregression constructions for very short-term wind energy forecasting. Wind Energy 20, 657–675. https://doi.org/10.1002/we.2029 (2017).Article
ADS
Google Scholar
Moayedi, H. & Mosavi, A. An innovative metaheuristic technique for solar vitality administration via a neural networks framework. Energies 14, 1196 (2021).
Google Scholar
Abedinia, O., Amjady, N. & Ghadimi, N. Solar vitality forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34, 241–260 (2018).MathSciInternet
Google Scholar
Galván, I. M., Valls, J. M., Cervantes, A. & Aler, R. Multi-objective evolutionary optimization of prediction intervals for solar vitality forecasting with neural networks. Inf. Sci. 418, 363–382 (2017).
Google Scholar
Tran, D.-H., Luong, D.-L. & Chou, J.-S. Nature-inspired metaheuristic ensemble mannequin for forecasting vitality consumption in residential buildings. Energy 191, 116552 (2020).
Google Scholar
Halabi, L. M., Mekhilef, S. & Hossain, M. Performance analysis of hybrid adaptive neuro-fuzzy inference system fashions for predicting month-to-month international solar radiation. Appl. Energy 213, 247–261 (2018).ADS
Google Scholar
Zhao, Y., Moayedi, H., Bahiraei, M. & Foong, L. Ok. Employing TLBO and SCE for optimum prediction of the compressive power of concrete. Smart Struct. Syst. 26, 753. https://doi.org/10.12989/sss.2020.26.6.753 (2020).Louzazni, M. et al. Metaheuristic algorithm for photovoltaic parameters: comparative research and prediction with a firefly algorithm. Appl. Sci. 8, 339 (2018).
Google Scholar
Bechouat, M. et al. Parameters identification of a photovoltaic module in a thermal system utilizing meta-heuristic optimization strategies. Int. J. Energy Environ. Eng. 8, 331–341 (2017).
Google Scholar
Zhou, G., Moayedi, H. & Foong, L. Ok. Teaching–learning-based metaheuristic scheme for modifying neural computing in appraising vitality efficiency of constructing. Eng. Comput. 37, 3037–3048. https://doi.org/10.1007/s00366-020-00981-5 (2021).Article
Google Scholar
Vaisakh, T. & Jayabarathi, R. Analysis on clever machine learning enabled with meta-heuristic algorithms for solar irradiance prediction. Evolutionary Intelligence, 1–20 (2020).Abedinia, O., Amjady, N. & Ghadimi, N. Solar vitality forecasting based on hybrid neural network and improved metaheuristic algorithm. Comput. Intell. 34(1), 241–260. https://doi.org/10.1111/coin.12145 (2018).Article
MathSciInternet
Google Scholar
Abdalla, O., Rezk, H. & Ahmed, E. M. Wind pushed optimization algorithm based international MPPT for PV system underneath non-uniform solar irradiance. Solar Energy 180, 429–444 (2019).ADS
Google Scholar
Lu, C. et al. Split-core magnetoelectric present sensor and wi-fi present measurement software. Measurement 188, 110527 (2022).
Google Scholar
Li, P., Hu, J., Qiu, L., Zhao, Y. & Ghosh, B. Ok. A distributed financial dispatch technique for energy–water networks. IEEE Trans. Control Netw. Syst. 9, 356–366 (2021).MathSciInternet
Google Scholar
Zheng, X. et al. Combustion traits and thermal decomposition mechanism of the flame-retardant cable in city utility tunnel. Case Stud. Thermal Eng. 44, 102887 (2023).ADS
Google Scholar
Zhang, L. et al. Development of geopolymer-based composites for geothermal vitality purposes. J. Clean. Product. 419, 138202 (2023).CAS
Google Scholar
Cheng, B., Zhu, D., Zhao, S. & Chen, J. Situation-aware IoT service coordination utilizing the event-driven SOA paradigm. IEEE Trans. Netw. Serv. Manag. 13, 349–361 (2016).
Google Scholar
Alkabbani, H., Ahmadian, A., Zhu, Q. & Elkamel, A. Machine learning and metaheuristic strategies for renewable energy forecasting: a latest overview. Front. Chem. Eng. 3, 665415. https://doi.org/10.3389/fceng.2021.665415 (2021).Article
Google Scholar
Gumar, A. Ok. & Demir, F. Solar photovoltaic energy estimation utilizing meta-optimized neural networks. Energies 15, 8669 (2022).
Google Scholar
Aissaoui, A., Belhaouas, N., Hadjrioua, F., Bakria, Ok. & Aloui, I. in Artif. Intell. Renew. Energy Trans. 4, 592–603 (Springer).Feng, Y., Cui, N., Chen, Y., Gong, D. & Hu, X. Development of data-driven fashions for prediction of each day international horizontal irradiance in northwest China. J. Clean. Product. 223, 136–146 (2019).
Google Scholar
Zang, H. et al. Short-term international horizontal irradiance forecasting based on a hybrid CNN-LSTM mannequin with spatiotemporal correlations. Renew. Energy 160, 26–41 (2020).
Google Scholar
Faramarzi, A., Heidarinejad, M., Stephens, B. & Mirjalili, S. Equilibrium optimizer: A novel optimization algorithm. Knowl.-Based Syst. 191, 105190. https://doi.org/10.1016/j.knosys.2019.105190 (2020).Article
Google Scholar
Elsheikh, A. H., Shehabeldeen, T. A., Zhou, J., Showaib, E. & Abd Elaziz, M. Prediction of laser reducing parameters for polymethylmethacrylate sheets utilizing random vector purposeful hyperlink network built-in with equilibrium optimizer. J. Intell. Manuf. https://doi.org/10.1007/s10845-020-01617-7 (2020).Article
Google Scholar
Bayraktar, Z., Komurcu, M. & Werner, D. H. in 2010 IEEE Antennas and Propagation Society International Symposium. 1–4 (IEEE).Moayedi, H., Bui, D. T. & Ngo, P. T. T. Shuffled Frog Leaping Algorithm and Wind-Driven Optimization Technique Modified with Multilayer Perceptron. (2020).Bayraktar, Z. Adaptive Wind Driven Optimization Trained Artificial Neural Networks. arXiv preprint arXiv:1911.08942 (2019).Kashan, A. H. A brand new metaheuristic for optimization: optics impressed optimization (OIO). Comput. Op. Res. 55, 99–125 (2015).MathSciInternet
Google Scholar
ÖZDEMİR, M. & Öztürk, D. Comparative efficiency evaluation of optimum PID parameters tuning based on the optics impressed optimization strategies for computerized technology management. Energies 10, 2134 (2017).James, J. & Li, V. O. A social spider algorithm for international optimization. Appl. Soft Comput. 30, 614–627 (2015).
Google Scholar
James, J. & Li, V. O. A social spider algorithm for fixing the non-convex financial load dispatch downside. Neurocomputing 171, 955–965 (2016).
Google Scholar
Sankar, V. U., Basha, C. H., Mathew, D., Rani, C. & Busawon, Ok. in Soft Computing for Problem Solving 925–940 (Springer, 2020).Jalili, S. & Husseinzadeh Kashan, A. Optimum discrete design of metal tower constructions utilizing optics impressed optimization technique. Struct. Des. Tall Spec. Build. 27, e1466 (2018).
Google Scholar
Özdemir, M. T. & Öztürk, D. (ICNES, 2016).El-Bages, M. & Elsayed, W. Social spider algorithm for fixing the transmission growth planning downside. Electric Power Syst. Res. 143, 235–243 (2017).
Google Scholar
Ewees, A. A., El Aziz, M. A. & Elhoseny, M. in 2017 eighth worldwide convention on computing, communication and networking applied sciences (ICCCNT). 1–6 (IEEE).Abdi, H. & Williams, L. J. Principal part evaluation. Wiley Interdiscip. Rev.: Comput. Stat. 2, 433–459 (2010).
Google Scholar
Mehrabi, M., Scaioni, M. & Previtali, M. Forecasting Air Quality in Kiev During 2022 Military Conflict Using Sentinel 5P and Optimized Machine Learning. IEEE Trans. Geosci. Remote Sens. (2023).
https://www.nature.com/articles/s41598-024-52462-0