Jie Li,∗ Yuxiang Peng,∗∗ Zhou Yang,∗ and Juntao Pan∗
[1] P.S. Badal and R. Sinha, A multi-objective performance-based seismic design framework for building typologies,Earthquake Engineering & Structural Dynamics, 51(6), 2022,1343–1362. [2] B.Y. Yan, C. Yang, and F. Chen, FDNet: A deep learningapproach with two parallel cross encoding pathways forprecipitation nowcasting, Journal of Computer Science andTechnology, 38(5), 2023, 1002–1020. [3] J.S. Pan, N. Liu, S.C. Chu, and T. Lai, An efficientsurrogate-assisted hybrid optimization algorithm for expensiveoptimization problems, Information Sciences, 561(2), 2020,304–325. [4] Z. Serat, S.A.Z. Fatemi, and S. Shirzad, Design and economicanalysis of on-grid solar rooftop PV system using PVsystsoftware, Archives of Advanced Engineering Science, 1(1),2023, 63–76. [5] B. Lin, H. Chen, Y. Liu, Q. He, and Z. Li, A preference-basedmulti-objective building performance optimization methodfor early design stage, Building Simulation, 14(3), 2021,477–494. [6] A. W. Kumar, M. D. Mufti, and M. Y. Zargar, Dynamic per-formance enhancement of wind penetrated power system usingSMES as virtual AQ2 synchronous generator, InternationalJournal of Power and Energy Systems, 41(2), 2021, 91–98. [7] F. Jiao, Y. Deng, D. Li, B. Wei, and Q. Xiang, Use ofenergy coupling devices for virtual power plant operation,International Journal of Power and Energy Systems, 41(3),2021, 183–191. [8] J. Li, D. Wang, H. Jia, G. Wu, and H. Xiong, Prospectsof key technologies of integrated energy systems for ruralelectrification in China, Global Energy Interconnection, 4(1),2021, 3–17. [9] K.C. Raghu, F. Jarno, and R. Tapio, Public perception onthe sustainable energy transition in rural Finland: A multi-criteria approach, Circular Economy and Sustainability, 3(2),2023, 735–755. [10] N. Yimen, L. Monkam, D. Tcheukam-Toko, B. Musa, R.Abang, L.F. Fombe, S. Abbasoglu, and M. Dagbasi, Optimaldesign and sensitivity analysis of distributed biomass-basedhybrid renewable energy systems for rural electrification:Case study of different photovoltaic/wind/battery-integratedoptions in Babadam, Northern Cameroon, IET RenewablePower Generation, 16(14), 2022, 2939–2956. [11] F. Li, K. He, and R. Zhu, Rural low-carbon energy developmentin the information age: Can internet access drive the farmerto participate in personal carbon trading schemes related tobioenergy? Sustainable Development, 31(3), 2023, 1417–1432. [12] Y. Tan, Y. Shen, X. Yu, and X. Lu, Low-carbon economicdispatch of the combined heat and power-virtual power plants:A improved deep reinforcement learning-based approach, IETRenewable Power Generation, 17(4), 2023, 982–1007. [13] X. Yang, N. Du, G. Sun, C. Fang, and Y. Tian, Bi-leveloptimization dispatch of virtual power plants considering thedemand response, Journal of Electric Power Science andTechnology,37(2), 2022, 137–146. [14] H. Zhao, C. Zhang, Y. Zhao, and X. Wang, Low-carboneconomic dispatching of multi-energy virtual power plant withcarbon capture unit considering uncertainty and carbon market,Energies, 15(19), 2022, 7225–7225. [15] A. Nasim, L. Burattini, and M.F. Fatheh, Solution of linearand non-linear boundary value problems using population-distributed parallel differential evolution, Journal of ArtificialIntelligence and Soft Computing Research, 9(3), 2019,205–218. [16] L. Yuanjun, L. Zhang, and Y. Li, Parallel transferevolution algorithm, Applied Soft Computing, 75(1), 2019,686–701. [17] F.J. Solis-Munoz, R.A. Osornio-Rios, and R.J. Romero-Troncoso Differential evolution implementation forpower quality disturbances monitoring using OpenCL,Advances in Electrical and Computer Engineering, 19(2),2019, 13–22. [18] M. Zuo and G. Dai, P-lsGOF: A parallel learning-selection-based global optimization framework, Journal of Intelligentand Fuzzy Systems, 39(5), 2020, 7333–7361. [19] T. Janus and S. Engell, Iterative process design with surrogate-assisted global flowsheet optimization, Chemie IngenieurTechnik, 93(12), 2021, 2019–2028. [20] C.L. Tsai and G. Fredrickson, Using particle swarmoptimization and self-consistent field theory to discover globallystable morphologies of block copolymers, Macromolecules,55(12), 2022, 5249–5262. [21] A. Mohammad and F. Mahjabeen, Revolutionizing solarenergy with ai-driven enhancements in photovoltaic tech-nology, BULLET: Jurnal Multidisiplin Ilmu, 2(4), 2023,1174–1187. [22] M. Seki, Determination of relationships between stand variablesand parameters of weibull function for Fagus Orientalis Libskystands, Kastamonu University Journal of Forestry Faculty,22(1), 2022, 68–77. [23] I. Dagal, B. Akn, and E. Akboy, Improved salp swarm algorithmbased on particle swarm optimization for maximum powerpoint tracking of optimal photovoltaic systems, InternationalJournal of Energy Research, 46(7), 2022, 8742–8759. [24] P. Sharma, Z. Said, A. Kumar, S. Nizetic, A. Pandey, A.T.Hoang, Z. Huang, A. Afzal, C. Li, A.T. Le, and X.P. Nguyen,Recent advances in machine learning research for nanofluid-based heat transfer in renewable energy system, Energy &Fuels, 36(13), 2022, 6626–6658. [25] IB. Mansir, E.H.B. Hani, H. Ayed, and C. Diyoke, Dynamicsimulation of hydrogen-based zero energy buildings withhydrogen energy storage for various climate conditions,International Journal of Hydrogen Energy, 47(62), 2022.26501–26514. [26] C. Hebbi and H. Mamatha, Comprehensive dataset buildingand recognition of isolated handwritten Kannada charactersusing machine learning models, Artificial Intelligence andApplications, 1(3), 2023, 179–190. [27] M. Gheisari, H. Hamidpour, Y. Liu, P. Saedi, A. Raza, A.Jalili, H. Rokhsati, and R. Amin, Data mining techniques forweb mining: A survey, Artificial Intelligence and Applications,1(1), 2023, 3–10. [28] W. Wu, X. Zhang, J. Zhang, Y. He, and W. Bai, Animproved Hausdorff distance method for locating single phaseto ground fault in neutral non-effectively grounded system,IET Generation, Transmission & Distribution, 15(19), 2021,2747–2759.
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