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Renewable Energy (78)
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Almonacid, F., Rus, C., Hontoria, L., Fuentes, M., & Nofuentes, G. (2009). Characterisation of Si-crystalline PV modules by artificial neural networks. Renewable Energy, 34(4), 941–949.

Authors 5
  1. F. Almonacid (first)
  2. C. Rus (additional)
  3. L. Hontoria (additional)
  4. M. Fuentes (additional)
  5. G. Nofuentes (additional)
References 52 Referenced 105
  1. {'key': '10.1016/j.renene.2008.06.010_bib1', 'first-page': '2007', 'article-title': 'Systemes solares', 'volume': '178', 'author': 'Photovoltaic Barometer', 'year': '2007', 'journal-title': 'Le journal des energies Renouvelables'} / Le journal des energies Renouvelables / Systemes solares by Photovoltaic Barometer (2007)
  2. Bücher K. Do we need site-dependent and climate-dependent module rating? In: Proceedings of 23rd IEEE photovoltaic specialists conference, Louisville, KY; May 10–14, 1993. p. 1056–62. (10.1109/PVSC.1993.346978)
  3. Anderson D, Bishop J, Dunlop E. Energy rating of photovoltaic modules. In: 16th European photovoltaic solar energy conference, Glasgow; 2000. p. 2087–91. (10.4324/9781315074405-6)
  4. 10.1002/(SICI)1099-159X(199903/04)7:2<137::AID-PIP249>3.0.CO;2-C / Progress in Photovoltaics: Research and Applications / Quality control of wide collections of PV modules: lessons learned from the IES experience by Caamaño (1999)
  5. Wagner A. Photovoltaic measurement relevant to the energy yield. In: Proceedings of the third WCPEC, Osaka; 2003.
  6. {'year': '1995', 'series-title': 'Guidelines for the assessment of photovoltaic plants', 'author': 'Blaesser', 'key': '10.1016/j.renene.2008.06.010_bib6'} / Guidelines for the assessment of photovoltaic plants by Blaesser (1995)
  7. {'year': '1982', 'series-title': 'Solar cells: operating principles, technology and system applications', 'author': 'Green', 'key': '10.1016/j.renene.2008.06.010_bib7'} / Solar cells: operating principles, technology and system applications by Green (1982)
  8. 10.1016/0379-6787(86)90126-2 / Solar cells / Translation of device performance measurement to reference conditions by Osterwald (1986)
  9. IEC 60891. Procedure for temperature and irradiance corrections to measurement V–I characteristics of crystalline silicon photovoltaic devices. First ed. 04-1987. Geneve. p. 7–14.
  10. Commission of the European Communities, Joint Research Centre of Ispra. Guidelines for the assessment of photovoltaic plants. Document C. Issue 4.1; 1993. p. 10–4.
  11. 10.1002/pip.403 / Progress in Photovoltaics: Research and Applications / A method for modelling the current–voltage curve on a PV module for outdoor conditions by Marion (2002)
  12. 10.1002/pip.551 / Progress in Photovoltaics: Research and Applications / Current–voltage curve translation by bilinear interpolation by Marion (2004)
  13. Hester SL, Town TU, Clements WT, Stolte WJ. PVUSA: lessons learned from startup and early operation. In: Proceeding of the 21st IEE PV specialists conference, Orlando, USA; 1990. p. 937–43. (10.1109/PVSC.1990.111757)
  14. King DL, Kratochvil JA, Boyson WE, Bower W. Field experience with a new performance characterization procedure for photovoltaic arrays. In: Proceeding of the second world conference and exhibition on photovoltaic solar energy conversion, Vienna, Austria. Joint Research Center Report EUR 18656; 1998. p. 1947–52.
  15. Hontoria L, Aguilera J, Nofuentes G, Almonacid F, De la Casa J. Contribution to quality control of PV modules: a new standard test conditions (stc) V–I curve conversion method using neural networks. In: Proceedings of world renewable energy congress on CD-ROM, Aberdeen, United Kingdom; 2005.
  16. Almonacid F, Hontoria L, Aguilera J, Nofuentes G. Improvement in the quality control of PV modules using neural network. In: 21st European photovoltaic solar energy conference and exhibition, Dresden, Germany; 2006.
  17. 10.1023/A:1012031827871 / Journal of Intelligent and Robotic Systems / Recurrent neural supervised models for generating solar radiation by Hontoria (2001)
  18. 10.1016/S0038-092X(02)00010-5 / Solar Energy / Generation of hourly irradiation synthetic series using the neural network multilayer perceptron by Hontoria (2002)
  19. 10.1109/37.581297 / IEEE Control Systems Magazine / A systematic classification of neural-network-based control by Agarwal (1997)
  20. {'year': '1994', 'series-title': 'Neural networks. A comprehensive foundation', 'author': 'Haykin', 'key': '10.1016/j.renene.2008.06.010_bib20'} / Neural networks. A comprehensive foundation by Haykin (1994)
  21. {'key': '10.1016/j.renene.2008.06.010_bib21', 'series-title': 'Artificial intelligence in energy and renewable energy systems', 'first-page': '163', 'article-title': 'Artificial neural networks applied in PV systems and solar radiation', 'volume': 'vol. 5', 'author': 'Hontoria', 'year': '2006'} / Artificial intelligence in energy and renewable energy systems / Artificial neural networks applied in PV systems and solar radiation by Hontoria (2006)
  22. 10.1016/j.apenergy.2005.06.003 / Applied Energy / An adaptative wavelet-network model for forecasting daily total solar by Mellit (2006)
  23. 10.1016/j.solener.2004.12.006 / Solar Energy / A simplified model for generating sequences of global radiation data for insolates sites: using neural network and library of Markov transition matrices by Mellit (2005)
  24. 10.1109/60.391904 / IEEE Transactions on Energy Conversion / Identification of optimal operating point for PV modules using neural networks for real time maximum power tracking control by Takashi (1995)
  25. 10.1109/60.464880 / IEEE Transactions on Energy Conversion / Evaluation of neural networks based real time maximum power tracking controller for PV system by Himaya (1995)
  26. 10.1109/60.629709 / IEEE Transactions on Energy Conversion / Neural network based estimation of maximum power generation from PV module using environmental information by Himaya (1997)
  27. 10.1016/S0038-092X(00)00085-2 / Solar Energy / ANN based peak power tracking for PV supplied motors by Veerachary (2000)
  28. 10.1016/S0960-1481(03)00126-5 / Renewable Energy / Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks by Bahgat (2004)
  29. 10.1016/j.renene.2004.09.011 / Renewable Energy / Maximum power point tracking controller for PV systems using neural networks by Bahgat (2005)
  30. 10.1049/ip-gtd:20000605 / IEE Proceedings: Generation, Transmission and Distribution / Application of radial basis function networks for solar-array modelling and maximum power-point prediction by Al-Amoudi (2000)
  31. 10.1109/TIE.2003.814762 / IEEE Transactions on Industrial Electronics / Neural-network-based maximum-power-point tracking of coupled-inductor interleaved-boost-converter-supplied PV system using fuzzy controller by Veerachary (2003)
  32. 10.1016/j.renene.2004.11.012 / Renewable Energy / An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria by Mellit (2005)
  33. 10.1016/j.renene.2006.01.002 / Renewable Energy / Modelling and simulation of stand-alone photovoltaic system using an adaptative artificial neural network by Mellit (2007)
  34. 10.1016/j.enconman.2005.07.007 / Energy Conversion & Management / Neural network based solar cell model by Karapete (2006)
  35. 10.1049/ip-epa:19990116 / IEE Proceedings on Electric Power Applications / Development of a photovoltaic array model for use in power-electronics simulation studies by Gow (1999)
  36. 10.1016/S0960-1481(01)00056-8 / Renewable Energy / Selecting a suitable model for characterizing photovoltaic devices by de Blas (2002)
  37. Hontoria L, Aguilera J, Zufiria PJ. A tool for obtaining the lolp curves for sizing off-grid photovoltaic systems based in neural networks. In: Proceedings of the third WCPEC, Osaka, Japan; 2003.
  38. 10.1016/0379-6787(82)90008-4 / Solar Cells / Determination of the two-exponential solar cell equation parameters from empirical data by Araujo (1982)
  39. Fuentes M, Nofuentes G, Aguilera J, Talavera DL, Castro M. Experimental validation of algebraic methods to predict the outdoors electrical performance of monocrystalline silicon PV modules in Southern Europe climates. In: Proceedings of ISES 25th world solar congress, Orlando, USA; 2005. p. 1286–91.
  40. 10.1109/79.180705 / IEEE Signal Processing Magazine / Progress in supervised neural networks. What's new since Lippmann by Hush (1993)
  41. {'year': '1995', 'series-title': 'Self-organising maps', 'author': 'Kohonen', 'key': '10.1016/j.renene.2008.06.010_bib41'} / Self-organising maps by Kohonen (1995)
  42. 10.1007/BF02551274 / Mathematics of Control, Signals, and Systems / Approximation by superpositions of a sigmoidal function by Cybenko (1989)
  43. 10.1016/0893-6080(89)90003-8 / Neural Networks / On the approximate realisation of continuous mappings by neural networks by Funahashi (1989)
  44. 10.1016/0893-6080(89)90020-8 / Neural Networks / Multilayer feedforward networks are universal approximators by Hornik (1989)
  45. {'key': '10.1016/j.renene.2008.06.010_bib45', 'article-title': 'Learning internal representations by error backpropagation', 'volume': 'vol. 1', 'author': 'Rumelhart', 'year': '1986'} / Learning internal representations by error backpropagation by Rumelhart (1986)
  46. Werbos P. Beyond regression: new tools for prediction and analysis in the behavioural sciences. Ph.D. dissertation, Committee on Appl. Math., Harvard Univ., Cambridge, MA; 1974.
  47. 10.1109/72.248452 / IEEE Transactions on Neural Networks / Pruning algorithms – a survey by Reed (1993)
  48. 10.1109/61.368408 / IEEE Transactions on Power Delivery / The classification of power system disturbance waveforms using a neural network approach by Ghosh (1995)
  49. {'year': '1992', 'series-title': 'Introduction to artificial neural systems', 'author': 'Zurada', 'key': '10.1016/j.renene.2008.06.010_bib49'} / Introduction to artificial neural systems by Zurada (1992)
  50. {'key': '10.1016/j.renene.2008.06.010_bib50', 'article-title': 'Model selection in neural networks: some difficulties', 'author': 'Curry', 'year': '2004', 'journal-title': 'European Journal of Operational Research'} / European Journal of Operational Research / Model selection in neural networks: some difficulties by Curry (2004)
  51. King DL, Kratochvil JA, Boyson WE. Temperature coefficient for PV modules and arrays: measurement methods, difficulties and results. In: IEEE 26th PVSC 3, Anaheim, CA; 1997.
  52. Anderson D, Sample T, Dunlop E. Obtaining module energy rating from standard laboratory measurements. In: 17th European photovoltaic solar energy conference, Munich; 2001. p. 832–35.
Dates
Type When
Created 17 years, 1 month ago (July 28, 2008, 12:47 p.m.)
Deposited 7 months ago (Jan. 31, 2025, 1:04 a.m.)
Indexed 2 months, 1 week ago (June 19, 2025, 12:27 p.m.)
Issued 16 years, 5 months ago (April 1, 2009)
Published 16 years, 5 months ago (April 1, 2009)
Published Print 16 years, 5 months ago (April 1, 2009)
Funders 0

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@article{Almonacid_2009, title={Characterisation of Si-crystalline PV modules by artificial neural networks}, volume={34}, ISSN={0960-1481}, url={http://dx.doi.org/10.1016/j.renene.2008.06.010}, DOI={10.1016/j.renene.2008.06.010}, number={4}, journal={Renewable Energy}, publisher={Elsevier BV}, author={Almonacid, F. and Rus, C. and Hontoria, L. and Fuentes, M. and Nofuentes, G.}, year={2009}, month=apr, pages={941–949} }