Artificial neural networks applied in PV systems and solar radiation

Artificial Intelligence in Energy and Renewable Energy Systems

Volumn , Issue , 2006, Pages 163-200

  Hontoria, Leocadio ^a,b   Aguilera, Jorge ^a,b   Almonacid, Florencia ^a,b   Nofuentes, Gustavo ^a,b   Zufiria, Pedro ^c  

^a Polytechnics School   (Spain)

^b UNIVERSITY OF JAÉN   (Spain)

^c UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

Author keywords

Artificial neural networks; Multilayer perceptron; Photovoltaic systems; Solar radiation

Indexed keywords

EID: 84895362739     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (68)

1. Aguiar R., Collares-Pereira M., 1991. Statistical properties of hourly global radiation. Solar Energy, Vol. 48, No. 3, pp.157-167.
2. Aguiar R., Collares-Pereira M., 1992. TAG: A time-dependent, autorregressive, Gaussian model for generating synthetic hourly radiation. Solar Energy, Vol. 49, No. 3, pp.167-174.
3. Alawi S.M., Hinai H.A., 1998. An ANN-based approach for predicting global radiation in locations with no direct measurement instrumentation. Renewable Energy, Vol. 14, No. 1-4, pp.199-204.
4. Agarwal M., 1997. A systematic classification of neural-network-based control. IEEE Control Systems Magazine, Vol. 17, No. 2, pp.75-93.
5. Bahgat A.B.G., Helwa N.H., Ahamd G.E., El Shenawy E.T., 2004. Estimation of the maximum power and normal operating power of a photovoltaic module by neural networks. Renewable Energy, Vol. 29, pp.443-457.
6. Bahgat A.B.G., Helwa N.H., Ahamd G.E., El-Shenawy E.T., 2005. Maximum power point tracking controller for PV systems using neural networks. Renewable Energy, Vol. 30, pp.1257-1268.
7. Balouktsis A., Tsalides Ph., 1986. Stochastic simulation model of hourly total solar radiation. Solar Energy, Vol. 37, No. 2, pp.119-126.
8. Barra L., Cataloni S., Fontana F., Lavorante F., 1984. An analytical method to determine the optimal size of a photovoltaic plant. Solar Energy, Vol. 33, No. 6, pp.509-514.
9. Barron R., 1993. Universal approximation bounds for superpositions of a sigmoidal function. IEEE Transactions on Information Theory, Vol. 39, pp.930-945.
10. Bartoli B., Cuomo V., Fontana F., Serio C., Silvestrini V., 1984. The design of photovoltaic plants: An optimization procedure. Applied Energy, Vol. 18, pp.37-47.
11. Blaesser G., Munro D., 1995. Guidelines for the assessment of photovoltaic plants. Document C. Issue 2. Report EUR 16340 EN. Commission of the European Communities, Joint Research Centre of Ispra.
12. Bochud F., Nygard-Ferguson M., Scartezzini J.L., 1991. Stochastic models of hourly data of solar radiation and ambient temperature. Proceedings of ISES World Renewable Energy Conference on CD-ROM, Denver, Colorado, USA.
13. Caamaño E., Lorenzo E., Zilles R., 1999. Quality control of wide collections of PV modules: lessons learned from the IES experience. Progress in Photovoltaics: Research and Applications, Vol. 7, pp.137-149.
14. Chapman R.N., 1987. Sizing handbook for stand alone photovoltaic/storage systems. Sandia National Laboratories, April 1987.
15. Curtiss PS, Brandemuehl MJ, Kreider JF., 1995. Energy management in central HVAC plants using neural networks. In: Haberl J.S., Nelson R.M., Culp C.C. (editors). The use of artificial intelligence in building systems. ASHRAE, pp. 199-216.
16. Cybenko G., 1989. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, Vol. 2, pp.303-314.
17. Dagleman L. O., 1976. A weather simulation model for building analysis, ASHRAE Symposium on Weather Data, Seattle, WA Annual Meeting, June 1976. ASHRAE Transactions, pp. 435-447.
18. Egido M.A., Lorenzo E., 1992. The sizing of stand alone PV systems: a review and proposed method. Solar Energy Materials and Solar Cells, Vol. 26, pp.51-69.
19. Excell R. H. B., 1981. A mathematical model for solar radiation in south-east Asia. Solar Energy, Vol. 26, pp.161-168.
20. Fausset L., 1994. Fundamentals of neural networks. Prentice Hall.
21. Funahashi K., 1989. On the approximate realisation of continuous mappings by neural networks. Neural Networks, Vol. 2, pp.183-192.
22. Gansler, R. Klein, S.A. Beckman, W.A., 1994. Assessment of the accuracy of generated meteorological data for use in solar energy simulation studies. Solar Energy, Vol. 53. No. 3, pp. 279-287.
23. Graham V.A., Hollands K.G.T, Unny T.E., 1988. A time series model for Kt with application to global synthetic weather generation. Solar Energy, Vol. 40, No. 3, pp.269-279.
24. Graham V.A., Hollands K.G.T, 1990. A method to generate synthetic hourly solar radiation globally. Solar Energy, Vol. 44, No. 6, pp. 333-341.
25. Goh T., Tan K., 1977. Stochastic modelling and forecasting of solar radiation data. Solar Energy, Vol. 19, No. 6, pp.755-757.
26. Haykin S., 1994. Neural networks. A comprehensive foundation. Macmillan Publishing Company.
27. Hiyama T., 1995. Identification of optimal operating point for PV modules using neural networks for real time maximum power tracking control. IEEE. Transaction on Energy Conversion. Vol. 10, No. 2, pp. 360-367.
28. Hiyama T., Kouzuma S., Imakubo T., 1995. Evaluation of neural networks based real time maximum power tracking controller for PV system. IEEE Transaction on Energy Conversion, Vol. 10, No. 3, pp.543-548.
29. Hittle D.C., Pedersen C.O., 1981. Periodic and stochastic behaviour of weather data. ASHRAE Transactions, Vol. 87, No. 2, pp.545-557.
30. Hontoria L., Riesco J., Zufiria P.J., Aguilera J., 1999. Improved generation of hourly solar irradiation artificial series using neural networks. 5th International Conference on Engineering Applications of Neural Networks, EANN99, pp. 87-92.
31. Hontoria, L. Riesco, J. Zufiria, P. and Aguilera, J. 2000. Application of neural networks in the solar radiation field. Obtainment of solar radiation maps. 16th European Photovoltaic Solar Energy Conference and Exhibition. Glasgow, Vol. 3, Paper VD1.35, pp. 2539-2542.
32. Hontoria L., Aguilera J.,Riesco J., Zufíria P., 2001. Recurrent neural supervised models for generating solar radiation synthetic series. Journal of Intelligent and Robotic Systems, Vol. 31, pp.201-221.
33. Hontoria L., Aguilera J., Zufiria P.J., 2002. Generation of hourly irradiation synthetic series using the neural network multilayer perceptron. Solar Energy, Vol. 72, No. 5, pp.441-446.
34. Hontoria L., Aguilera J., Zufiria P.J., 2003. A tool for obtaining the LOLP curves for sizing off-grid photovoltaic systems based in neural networks. Proceedings of the 3rd World Conference on Photovoltaic Solar Energy Conversion on CD-ROM, Osaka, Japan.
35. Hontoria L., Aguilera J., Zufiria P.J., 2005a. A new approach for sizing stand alone photovoltaic systems based in neural networks. Solar Energy, Vol. 78, No. 2, pp. 313-319, (Special Issue ISES Solar World Congress 2003).
36. Hontoria L., Aguilera J., Nofuentes G., Almonacid F., de la Casa J., 2005b.Contribution to quality control of PV modules: a new standard test conditions (stc) V-I curve conversion method using neural networks. Proceedings of World Renewable Energy Congress on CD-ROM, Aberdeen, United Kingdom.
37. Hornik K., Stinchcombe M., White H., 1989. Multilayer feedforward networks are universal approximators. Neural Networks, Vol. 2, No. 5, pp.359-366.
38. Hush D. R., Horne B. G., 1993. Progress in supervised neural networks. What's new since Lippmann. IEEE Signal Process Magazine, Vol. 10, pp.8-31.
39. Iqbal, M., 1983. An introduction to solar radiation. Academic Press.
40. Kajl S., Roberge M.A., Lamarche L., Malinowski P., 1997. Evaluation of building energy consumption based on fuzzy logic and neural networks applications. Proceedings of CLIMA 2000 Conference on CD-ROM, Brussels, Belgium.
41. Kalogirou S.A., Panteliou S., Dentsoras S., 1996. Modelling of solar domestic water heating systems using artificial neural networks. Solar Energy, Vol. 65, No. 6, pp.335-342.
42. Kalogirou S.A., Neocleous C.C., Schizas C.N., 1997. Heating load estimation using artificial neural networks. Proceedings of CLIMA 2000 Conference on CD-ROM, Brussels, Belgium.
43. Kalogirou S., Eftekhari M., Pinnock D., 1999. Prediction of air flow in a single-sided naturally ventilated test room using artificial neural networks. Proceedings of Indoor Air'99, The 8th International Conference on Indoor Air Quality and Climate, Edinburgh, Scotland, Vol. 2, pp.975-980.
44. Kalogirou, S.A., 2000. Applications of artificial neural-networks for energy systems. Applied Energy, Vol. 67, No. 1-2, pp.17-35.
45. Kalogirou, S.A., 2001. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews, Vol. 5, No. 4, pp.373-401.
46. Kemmoku Y., Orita S., Nakagawa S., Skakibara T., 1999. Daily insolation forecasting using a multistage neural network. Solar Energy, Vol. 66, No. 3, pp.193-199.
47. Kohonen, T., 1995. Self-organising maps. Springer Verlag, Berlin Heidelberg.
48. Kreider J.F., Wang X.A., 1995. Artificial neural network demonstration for automated generation of energy use predictors for commercial buildings. In: Haberl, J.S., Nelson, R.M., Culp C.C. (editors). The use of artificial intelligence in building systems. ASHRAE, pp. 193-198.
49. Lalot S., 2000. Identification of the time parameters of solar collectors using artificial neural networks. Proceedings of Eurosun 2000 on CD-ROM. Copenhagen, Denmark.
50. Lippmann R.P., 1987.An introduction to computing with neural nets. IEEE ASSP Magazine, pp. 4-22, April 1987.
51. Liu B. Y. H. and Jordan R. C. 1960. The interrelationship and characteristics distribution of direct, diffuse and total solar radiation. Solar Energy, Vol. 4, No. 3, pp. 1-19.
52. Lorenzo E., Araujo G. L., Cuevas A., Egido M. A., Miñano J. C., Zilles R. 1994. Electricidad solar. Ingeniería de los sistemas fotovltaicos. Ed. Progensa.
53. Mellit A., Benghanem M., Hadj Arab A., Guessoum A., 2005. An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria. Renewable Energy, Vol. 30, No. 10, pp.1501-1524.
54. Mohandes M., Balchonaim A., Kassas R.S., Halawani T.O., 1998. Estimation of global solar radiation using artificial neural networks. Renewable Energy, Vol. 14, No. 1-4, pp.179-184.
55. Mustacchi C., Cena V. and Rocchi M., 1979. Stochastic simulation of hourly global radiation sequences. Solar Energy, Vol. 23, No. 1, pp.47-51.
56. Narendra, K.S., Parthasarathy, K., 1991. Gradient methods for the optimisation of dynamical systems containing neural networks. IEEE Transactions on Neural Networks, Vol. 2, No. 2, pp.252-262.
57. Negnevitsky M., Le T.L., 1995. Artificial neural networks application for current rating of overhead lines. IEEE Transactions on Neural Networks, pp. 418-222.
58. Rumelhart D. and MacClelland J.L., 1986. Learning internal representations by error backpropagation. Chapter 8 from Parallel Distributed Processing, Vol. 1: Foundations. The MIT Press.
59. Sfetsos A. and Coonick H., 2001. Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, Vol. 68. No. 2, pp. 169-178.
60. Sidrach-de-Cardona M., Mora López Ll., 1998. A simple model for sizing stand alone photovoltaic systems. Solar Energy Materials and Solar Cells, Vol. 55, No. 3, pp.199-214.
61. Sidrach-de-Cardona M., Mora López L., 1999. A general multivariate qualitative model for sizing stand alone photovoltaic systems. Solar Energy Materials and Solar Cells, Vol. 59, No. 3, pp.185-197.
62. Sözen A., Arcaklioglu E., Ozalp M., Caglar N., 2005. Forecasting based on neural network approach of solar potential in Turkey. Renewable Energy, Vol. 30, pp.1075-1090.
63. Takashi H., Imakubo T., 1995. Identification of optimal operating point of PV modules using neural networks for real time maximum power tracking control. IEEE Transactions on Energy Conversion, Vol. 10, No. 2, pp.360-367.
64. Veerachary M., Yadaiah N., 2000. ANN based peak power tracking for PV supplied motors. Solar Energy, Vol. 69, No. 4, pp.343-350.
65. Wagner A., 2003. Photovoltaic measurement relevant to the energy yield. Proceedings of the 3rd WCPEC on CD-ROM, Osaka, Japan.
66. Weigend A.S. Rumelhart D.E., Huberman B.A., 1990. Back-propagation, weight-elimination and time series prediction. Proceedings of the 1990 Connectionist models Summer School on CD-ROM. Morgan Kaufman.
67. Zufiria P.J., 1995. An overview of dynamic system control using neural networks. Neural Networks for Chemical Engineers (Editor: Bulsari A.B.). Elsevier, pp. 385-408, ISBN 0-444-82097-3.
68. Zufiria P.J., Vázquez A., Riesco J., Aguilera J., Hontoria L., 1999. A neural network approach for generating solar irradiation artificial series. Notes in Computer Science 1607, Mira Juan V. Sánchez-Andrés (Editors), Applications of Bio-Inspired Artificial Neural Networks. Work-Conference on Artificial and Natural Networks, II-874-883, Springer Verlag, Berlin Heidelberg.