Activitatea I.2.

Analiza comparativă a metodelor și tehnicilor inteligenței artificiale integrate în managementul sistemelor PV și sistematizarea acestora

  1.  Context

  2. Metode de predicție bazate pe modele

  3. Metode de predicție bazate pe date

  4. Bibliografie

Detalii

Bibliografie

  • Boyle, Renewable Energy: Power for a Sustainable Future. Oxford University Press, 2012.
  • Sorensen, Renewable Energy, Fourth Edition: Physics, Engineering, Environmental Impacts, Economics & Planning. Academic Press, 2010
  • Masters, Renewable and Efficient Electric Power Systems. New Jersey: Wiley-IEEE Press, 2004
  • E.Dragomir, F. Dragomir , “A Multi-Agent System for Energy Management in an Intelligent Microgrid”, Proceedings of the 18th SGEM GeoConference on Energy and Clean Technologies, 2018
  • Kohl, “The Development. In R. Wengenmayr, & T. Buhrke, Renewable Energy”, Weinheim: Wiley-VCH , 2008, pp. 4-14
  • Da Rosa, Fundamentals of Renewable Energy Processes. Oxford: Academic Press, 2012
  • Kemp, The Renewable Energy Handbook, Revised Edition: The Updated Comprehensive Guide to Renewable Energy and Independent Living. Tamworth: Aztext Press, 2009.
  • MacKay, Sustainable Energy – Without the Hot Air. Cambridge: UIT Cambridge Ltd, 2009
  • S. Kaltschmitt, Renewable Energy: Technology, Economics and Environment. Berlin: Springer, 2010.
  • Bryce, Power Hungry: The Myths of “Green” Energy and the Real Fuels of the Future. New York: PublicAffairs, 2011
  • Chiras, The Homeowner’s Guide to Renewable Energy: Achieving Energy Independence Through Solar, Wind, Biomass, and Hydropower. Gabriola Island: New Society Publishers, 2011
  • Dragomir, O.E. Dragomir, N.Olariu, A. Oprea, “ Power Quality Analysis of Grid Connected PV Power System”, Proceedings of the 18th SGEM GeoConference on Energy and Clean Technologies, 2018
  • Rapier, Power Plays: Energy Options in the Age of Peak Oil. Apress, 2012
  • Dragomir O.E., Dragomir, Forcasting of renewable energy load with radial basis function (RBF) neural networks. The 8th International Conference on Informatics in Control, Automation and Robotics, (pp. 409-412), 2011. Noordwijkerhout, Olanda
  • Dragomir O.E., F. Dragomir, A Fuzzy Approach to Intelligent Control of Low Voltage Electrical Networks with Distributed Power from Renewable Resources. IEEE Energy Conference & Exhibition , (pp. pp. 606-611), 2010. Manama, Bahrain.
  • Dragomir O.E., F. Dragomir, Improvement of energy consume from hybrid systems integrating renewable energy sources. Advanced Materials Research , vol. 512 – 515, pp. 1147-1150, 2012.
  • Dragomir O.E., F. Dragomir, An application oriented guideline for choosing a prognostic tool. AIP Conference Proceedings: 2nd Mediterranean Conference on Intelligent Systems and Automation (CISA ’09), (pp. vol. 1107, pp. 257-262), 2009.
  • Dragomir O.E., Dragomir Adaptive Neuro Fuzzy Forecasting of Renewable Energy Balance on Medium Term. The 18th IEEE Mediterranean Conference on Control and Automation, (pp. 551-556), 2010. Marrakech, Morocco.
  • Dragomir O.E., Dragomir Forecasting of Renewable Energy Balance on Medium Term. Large Scale Systems: Theory and Applications, (LSS2010), (pp. vol. 9, part. 1, 495-500), 2010. Villeneuve d’Ascq, France.
  • Dragomir O.E, F. Dragomir, E. Minca, “Fuzzy- multi agent hybrid system for decision support of consumers of energy from renewable sources”, International Conference on Mathematical Methods, Mathematical Models and Simulation in Science and Engineering: New Developments in Pure and Applied Mathematics, 2015, pp. 343-348.
  • Dimeas, N. D. Hatziargyriou, “A MAS Architecture for Microgrids Control”, The 13th International Conference on, Intelligent Systems Application to Power Systems, USA, 2005, pp. 402-406.
  • Dragomir, O.E. Dragomir, M.E. Ivan, s.a, “Optimal embedded system for two-axis tracking PV panels”, Journal of Applied and Physical Sciences, vol. 3(1), 2017, pp. 1-6
  • O.E., F. Dragomir, V. Stefan., E. Minca, “ Adaptive Neuro – Fuzzy Inference Systems – an Alternative Forecasting Tool for Prosumers”, Studies in Informatics and Control, vol. 24( 3), 2015.
  • J. Chatzivasiliadis, N. D.Hatziargyriou, A. L. Dimeas, “Development of an agent based intelligent control system for microgrids”, IEEE Power and Energy Society General Meeting – Conversion and Delivery of Electrical Energy in the 21st Century, Pittsburgh, USA, 2008, pp. 1-6.
  • D. J Mcarthur, E. M. Davidson, V. M. Catterson s.a, “Multi-agent systems for power engineering application”, Part I: Concepts, approaches, and technical challenges, IEEE Trans. on Power Systems, vol. 22, no. 4, 2007, pp. 1743-1752.
  • Dragomir, Dragomir O.E, “Forecasting of photovoltaic power generation by RBF neural networks”, Advanced Materials Research, Volume 918, Chapter 3: Power, Energy and Environment Engineering, 2014, pp. 200-205.
  • Pipattanasomporn, H. Feroze, S. Rahman, “Multi-agent systems in a distributed smart grid: Design and implementation”, IEEE/PES Power Systems Conference and Exposition, USA, 2009, pp. 1-8.
  • Rocabert, G. Azevedo, G.Vazquez s.a,” Intelligent control agent for transient to an island grid”, IEEE International Symposium on Industrial Electronics, Italy, 2010, pp. 2223-2228.
  • Jiang, “Agent-Based Control Framework for Distributed Energy Resources Microgrids”, IEEE/WIC/ACM International Conference on Intelligent Agent Technology, China, 2006, pp. 646-652.
  • Zheng, J. Cai, “A multi-agent system for distributed energy resources control in microgrid”, 5th International Conference on Critical Infrastructure , China, 2010, pp. 1-5.
  • Logenthiran, D. Srinivasan, D.Wong, “Multi-agent coordination for DER in MicroGrid”, IEEE International Conference on Sustainable Energy Technologies, Singapore, 2008, pp. 77-82.
  • Zhou, Y. Gu, Y. Ma, s.a., “Hybrid operation control method for micro-grid based on MAS”, IEEE International Conference on Progress in Informatics and Computing, China, 2010, pp. 72-75.
  • Kopp, JL Lean, “A new, lower value of total solar irradiance: evidence and climate significance”, Geophysical Research Letters 2011