Đánh giá các kỹ thuật học máy khác nhau như một mô hình thay thế cho lưới điện hạ thế

Balduin, S (2018) Surrogate models for composed simulation models in energy systems. Energy Inform 1(1):30. Balduin, S, Oest F, Blank-Babazadeh M, Nieße A, Lehnhoff S (2019) Tool-assisted surrogate selection for simulation models in energy systems In: 2019 Federated Conference on Computer Science and Information Systems (FedCSIS), 185–192.. IEEE. Balduin, S, Tröschel M, Lehnhoff S (2019) Towards domain-specific surrogate models for smart grid co-simulation. Energy Inform 2(1):27. Balduin, S, Westermann T, Puiutta E (2020) Evaluating different machine learning techniques as surrogate for low voltage grids. arXiv preprint arXiv:2006.12389. Baumann, M, Grundel S, Sauerteig P, Worthmann K (2019) Surrogate models in bidirectional optimization of coupled microgrids. at-Automatisierungstechnik 67(12):1035–1046. Beisheim, B, Rahimi-Adli K, Krämer S, Engell S (2019) Energy performance analysis of continuous processes using surrogate models. Energy 183:776–787. Blank, M, Gandor M, Niesse A, Scherfke S, Lehnhoff S, Sonnenschein M (2015) Regionally-specific scenarios for smart grid simulations In: 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG2015), 250–256.. IEEE. doi:10.1109/PowerEng.2015.7266328. Blank, M, Lehnhoff S (2014) Correlations in reliability assessment of agent-based ancillary-service coalitions In: 2014 Power Systems Computation Conference, 1–7.. IEEE. Danner, D, de Meer H (2019) State estimation in the power distribution system. ACM SIGMETRICS Perform Eval Rev 46(3):86–88. Dimitrov, N (2019) Surrogate models for parameterized representation of wake-induced loads in wind farms. Wind Energy 22(10):1371–1389. Donon, B, Clément R, Donnot B, Marot A, Guyon I, Schoenauer M (2020) Neural networks for power flow: Graph neural solver. Electr Power Syst Res 189:106547. Fischer, L, Memmen J-M, Veith EM, Tröschel M (2019) Adversarial resilience learning – towards systemic vulnerability analysis for large and complex systems. Ninth Int Conf Smart Grids Green Commun IT Energy-aware Technol 9(1):24–32. Frank, S, Steponavice I, Rebennack S (2012) Optimal power flow: a bibliographic survey ii. Energy Syst 3(3):259–289. Gerster, J (2018) Agent-based dynamic optimization of local controller configurations in converter dominated electricity grids using decoder functions. Energy Inform 1(1):46. Grisales-Noreña, L, Garzon-Rivera O, Ramırez-Vanegas C, Montoya O, Ramos-Paja C (2020) Application of the backward/forward sweep method for solving the power flow problem in dc networks with radial structure In: Journal of Physics: Conference Series, vol. 1448, 012012. Grundel, S, Sauerteig P, Worthmann K (2019) Surrogate models for coupled microgrids In: Progress in Industrial Mathematics at ECMI 2018, 477–483.. Springer, Cham, Switzerland. Gupta, S, Kazi F, Wagh S, Kambli R (2015) Neural network based early warning system for an emerging blackout in smart grid power networks. In: Buyya R Thampi SM (eds)Intelligent Distributed Computing, 173–183.. Springer, Cham. Hansen, J (2005) Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (4th ed.)Am Stat 59(1):113. Hochreiter, S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780. Kontis, EO, Kryonidis GC, Nousdilis AI, Malamaki K-ND, Papagiannis GK (2019) Power flow analysis of islanded ac microgrids In: 2019 IEEE Milan PowerTech, 1–6.. IEEE. Montoya, O, Escobar A, Garrido V (2020) Power flow solution in direct current grids using the linear conjugate gradient approach In: Journal of Physics: Conference Series, vol. 1448, 012016. Myers, RH, Montgomery DC, Anderson-Cook CM (2016) Response surface methodology: process and product optimization using designed experiments. John Wiley & Sons, Hoboken, New Jersey. John Wiley & Sons. Nagpal, S, Mueller C, Aijazi A, Reinhart CF (2019) A methodology for auto-calibrating urban building energy models using surrogate modeling techniques. J Build Perform Simul 12(1):1–16. Nilsson, M, Soder L, Olauson J, Eriksson R, Nordström L, Ericsson GN (2018) A machine learning method creating network models based on measurements In: 2018 Power Systems Computation Conference (PSCC), 1–7.. IEEE. Papathanassiou, S, Hatziargyriou N, Strunz K, et al (2005) A benchmark low voltage microgrid network In: Proceedings of the CIGRE Symposium: Power Systems with Dispersed Generation, 1–8.. CIGRE. Puiutta, E, Veith EM (2020) Explainable reinforcement learning: A survey. arXiv preprint arXiv:2005.06247. Ramachandran, KM, Tsokos CP (2014) Mathematical statistics with applications in r. Elsevier, San Diego, CA, USA. Elsevier. Simpson, TW, Poplinski JD, Koch PN, Allen JK (2001) Metamodels for computer-based engineering design: Survey and recommendations. Engineering with Computers 17(2):129–150. Slot, RM, Sørensen JD, Sudret B, Svenningsen L, Thøgersen ML (2020) Surrogate model uncertainty in wind turbine reliability assessment. Renew Energy 151:1150–1162. Spalthoff, C, Sarajlic D, Kittl C, Drauz S, Kneiske T, Rehtanz C, Braun M (2019) Simbench: Open source time series of power load, storage and generation for the simulation of electrical distribution grids In: International ETG-Congress 2019; ETG Symposium, 1–6.. VDE. Spyromitros-Xioufis, E, Tsoumakas G, Groves W, Vlahavas I (2016) Multi-target regression via input space expansion: treating targets as inputs. Mach Learn 104(1):55–98. Steinbrink, C (2016) A nonintrusive uncertainty quantification system for modular smart grid co-simulation. PhD thesis, Ph. D. thesis, University of Oldenburg. Steinbrink, C, Lehnhoff S, Rohjans S, Strasser TI, Widl E, Moyo C, Lauss G, Lehfuss F, Faschang M, Palensky P, et al (2017) Simulation-based validation of smart grids–status quo and future research trends In: International Conference on Industrial Applications of Holonic and Multi-Agent Systems, 171–185.. Springer. Syai’in, M, Soeprijanto A (2010) Neural network optimal power flow (nn-opf) based on ipso with developed load cluster method. World Acad Sci Eng Technol 72:48–53. Thurner, L, Scheidler A, Schafer F, Menke JH, Dollichon J, Meier F, Meinecke S, Braun M (2018) pandapower - an open source python tool for convenient modeling, analysis and optimization of electric power systems. IEEE Trans Power Syst 33(6):6510–6521. Vazquez-Canteli, J, Demir AD, Brown J, Nagy Z (2019) Deep neural networks as surrogate models for urban energy simulations In: Journal of Physics: Conference Series, vol. 1343, 012002.. IOP Publishing. Veerasamy, V, Wahab NIA, Ramachandran R, Madasamy B, Mansoor M, Othman ML, Hizam H (2020) A novel rk4-hopfield neural network for power flow analysis of power system. Appl Soft Comput:106346. Veith, EM, Wenninghoff N, Frost E (2020) The adversarial resilience learning architecture for ai-based modelling, exploration, and operation of complex cyber-physical systems. http://arxiv.org/abs/2005.13601. Accessed 1 Oct 2020. Xiang, M, Yu J, Yang Z, Yang Y, Yu H, He H (2020) Probabilistic power flow with topology changes based on deep neural network. Int J Electr Power Energy Syst 117:105650. Yuan, Z, Wang Y, Yi Y, Wang C, Zhao Y, Wen W (2019) Fast linear power flow algorithm for the study of steady-state performance of dc grid. IEEE Trans Power Syst 34(6):4240–4248. Zhao, J, Gómez-Expósito A, Netto M, Mili L, Abur A, Terzija V, Kamwa I, Pal B, Singh AK, Qi J, et al (2019) Power system dynamic state estimation: Motivations, definitions, methodologies, and future work. IEEE Trans Power Syst 34(4):3188–3198.