Advertisement

Artificial Intelligence for Photovoltaic Systems

  • Rami GhannamEmail author
  • Paulo Valente Klaine
  • Muhammad Imran
Chapter
Part of the Power Systems book series (POWSYS)

Abstract

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods.

References

  1. 1.
    US Senate Committee on Energy and Natural Resources, Transcript of the Testimony of Richard E. Smalley to the US Senate Committee on Energy and Natural Resources, US Government Publishing Office, 27 April 2004. https://www.gpo.gov/fdsys/pkg/CHRG-108shrg95239/html/CHRG-108shrg95239.htm. Accessed 23 Sept 2018
  2. 2.
    Bar A, Feigenbaum E (1981) The handbook of artificial intelligence. Morgan Kaufmann, San FranciscoGoogle Scholar
  3. 3.
    Samuel A (1959) Some studies in machine learning using the game of checkers. IBM J Res Dev 3(3):210–229MathSciNetCrossRefGoogle Scholar
  4. 4.
    Friedman J, Hastie T, Tibshirani R (2001) The elements of statistical learning. Springer, New YorkzbMATHGoogle Scholar
  5. 5.
    Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan Publishing Company, New YorkzbMATHGoogle Scholar
  6. 6.
    da Silva I, Spatti DH, Flauzino RA, Liboni L, dos Reis Alves S (2017) Artificial neural networks: a practical course. Springer, SwitzerlandCrossRefGoogle Scholar
  7. 7.
    Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772–781CrossRefGoogle Scholar
  8. 8.
    Khatib T, Mohamed A, Sopian K, Mahmoud M (2012) Assessment of artificial neural networks for hourly solar radiation prediction. Int J Photoenergy 2012:7 ppGoogle Scholar
  9. 9.
    Al-Daoud E (2009) A comparison between three neural network models for classification problems. J Artif Intell 2(2):56–64CrossRefGoogle Scholar
  10. 10.
    Sutton R, Barto A (1998) Reinforcement learning: an introduction. MIT press, CambridgeGoogle Scholar
  11. 11.
    Nwigbo S, Madhu B (2016) Expert system: a catalyst in educational development in Nigeria. IOSR J Mob Comput Appl 3(2):8–11Google Scholar
  12. 12.
    Darlington K (2010) The essence of expert systems. Pearson Education, EnglandGoogle Scholar
  13. 13.
    Holland J (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT Press, CambridgeCrossRefGoogle Scholar
  14. 14.
    Sivanandam S, Deepa S (2007) Introduction to genetic algorithms. Springer, BerlinzbMATHGoogle Scholar
  15. 15.
    Dorigo M (1992) The metaphor of the ant colony and its application to combinatorial optimization. PhD thesis, Politecnico di Milano, ItalyGoogle Scholar
  16. 16.
    Dorigo M, StÃijtzle T (2003) The ant colony optimization metaheuristic: algorithms, applications, and advances. Handbook of metaheuristics. International series in operations research and management science. Springer, BostonGoogle Scholar
  17. 17.
    Youssef A, El-Telbany M, Zekry A (2017) The role of artificial intelligence in photo-voltaic systems design and control: a review. Renew Sustain Energy Rev 78:72–79CrossRefGoogle Scholar
  18. 18.
    Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165CrossRefGoogle Scholar
  19. 19.
    Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl 2008:10 ppGoogle Scholar
  20. 20.
    Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In: Proceedings of IEEE international conference on evolutionary computationGoogle Scholar
  21. 21.
    Zadeh L (1965) Fuzzy sets. Inf Control 8:338–353CrossRefGoogle Scholar
  22. 22.
    Bose B (1994) Expert system, fuzzy logic, and neural network applications in power electronics and motion control. Proc IEEE 82(8):1303–1323CrossRefGoogle Scholar
  23. 23.
    Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculation by fast computing machines. J Chem Phys 21:1087–1091CrossRefGoogle Scholar
  24. 24.
    Kirkpatrick S, Gelatt C, Vecchi M (1983) Optimization by simulated annealing. Science 220(4598):671–680MathSciNetCrossRefGoogle Scholar
  25. 25.
    Hu C, White R (1983) Solar cells: from basics to advanced systems. McGraw-Hill, New YorkGoogle Scholar
  26. 26.
    Meinel A, Meinel M (1976) Applied solar energy. Addison-Wesley, ReadingGoogle Scholar
  27. 27.
    Kasap S (2018) Principles of electronic materials and devices. McGraw-Hill Education, New YorkGoogle Scholar
  28. 28.
    Mohandes M, Rehman S, Halawani T (1998) Estimation of global solar radiation using artificial neural networks. Renew Energy 14:179–184CrossRefGoogle Scholar
  29. 29.
    Rehman S, Mohandes M (2009) Estimation of diffuse fraction of global solar radiation using artificial neural networks. Energy Sources 31:974–984CrossRefGoogle Scholar
  30. 30.
    Lazzús J, Ponce A, Marín J (2011) Estimation of global solar radiation over the city of La Serena using a neural network. Appl Sol Energy 47(1):66–73CrossRefGoogle Scholar
  31. 31.
    Khatib T, Mohamed A, Sopian K, Mahmoud M (2012) Solar energy prediction for Malaysia using artificial neural networks. Int J Photoenergy 2012:1–16Google Scholar
  32. 32.
    Mellit A, Pavan A (2010) A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Sol Energy 84:807–821CrossRefGoogle Scholar
  33. 33.
    Mellit A (2008) Artificial intelligence techniques for modelling and forecasting of solar radiation data: a review. Int J Artif Intell Soft Comput 1:52–76CrossRefGoogle Scholar
  34. 34.
    SUNDA, Beijing Sunda Solar Energy Technology Company, Ltd. http://www.sundasolar.com/. Accessed 18 June 2018
  35. 35.
    Tsai H, Tu C, Su Y (2008) Development of generalized photovoltaic model using MATLAB/SIMULINK. In: Proceedings of the World congress on engineering and computer science, WCECS 2008, San Francisco, USA, 22–24 October 2008Google Scholar
  36. 36.
    The German Energy Society (Deutsche Gesellshaft fur Sonnenenergie) (2008) Photovoltaic systems: a guide for installers, architects and engineers. Routledge, AbingdonGoogle Scholar
  37. 37.
    Easwarakhanthan T, Bottin J, Bouhouch I, Boutrit C (1986) Nonlinear minimization algorithm for determining the solar cell parameters with microcomputers. Int J Sol Energy 4:1–12CrossRefGoogle Scholar
  38. 38.
    Al-Rashidi M, El-Naggar K, AlHajri M, Al-Othman A (2011) A new estimation approach for determining the I-V characteristics of solar cells. Sol Energy 85(7):1543–1550CrossRefGoogle Scholar
  39. 39.
    Karatepe E, Boztepe M, Colak M (2006) Neural network based solar cell model. Energy Convers Manag 47:1159–1178CrossRefGoogle Scholar
  40. 40.
    King D, Boyson W, Kratochvill J (2004) Photovoltaic array performance model. Sandia National Laboratories, Albuquerque, New MexicoGoogle Scholar
  41. 41.
    Townsend T (1989) A method for estimating the long-term performance of direct-coupled photovoltaic systems. Masters thesis, University of Wisconsin-Madison, Madison, WI, USAGoogle Scholar
  42. 42.
    Vergura S (2016) A complete and simplified datasheet-based model of PV cells in variable environmental conditions for circuit simulation. Energies 9:326CrossRefGoogle Scholar
  43. 43.
    El-Naggar K, AlRashidi M, AlHajri M, Al-Othman A (2012) Simulated annealing algorithm for photovoltaic parameters identification. Sol Energy 86:266–274CrossRefGoogle Scholar
  44. 44.
    Askarzadeh A, Rezazadeh A (2012) Parameter identification for solar cell models using harmony search-based algorithms. Sol Energy 86:3241–3249CrossRefGoogle Scholar
  45. 45.
    Sharma V, Colangelo A, Spagna G (1995) Photovoltaic technology: basic concepts, sizing of a stand alone photovoltaic system for domestic applications and preliminary economic analysis. Energy Convers Manag 36(3):161–174CrossRefGoogle Scholar
  46. 46.
    Tiwari G, Dubey S (2010) Fundamentals of photovoltaic modules and their applications. Royal Society of Chemistry Publishing, LondonGoogle Scholar
  47. 47.
    Lorenzo M, Egido E (1992) The sizing of stand alone PV-systems: a review and a proposed new method. Sol Energy Mater Sol Cells 26:51–69CrossRefGoogle Scholar
  48. 48.
    Ahmad G (2002) Photovoltaic powered rural zone family house in Egypt. Renew Energy 6:379–390CrossRefGoogle Scholar
  49. 49.
    Bhuiyan M, Asgar M (2003) Sizing of a stand-alone photovoltaic power system at Dhaka. Renew Energy 28:929–938CrossRefGoogle Scholar
  50. 50.
    Committee National Electric Code (2017) Article 690: Solar Photovoltaic (PV) Systems, NFPA 70. National Electric Code, National Fire Protection Association (NFPA)Google Scholar
  51. 51.
    Nikhil P, Subhakar D (2013) Sizing and parametric analysis of a stand-alone photovoltaic power plant. IEEE J Photovolt 3:776–784CrossRefGoogle Scholar
  52. 52.
    Hontoria L, Aguilera J, Zufiria P (2005) A new approach for sizing stand alone photovoltaic systems based in neural networks. Sol Energy 78:313–319CrossRefGoogle Scholar
  53. 53.
    Almonacid F, Rus C, Pérez-Higueras P, Hontoria L (2011) Calculation of the energy provided by a PV generator. Comparative study: conventional methods versus artificial neural networks. Energy 36:375–384CrossRefGoogle Scholar
  54. 54.
    Mellit A, Benghanem M, HadjArab A, Guessoume A (2005) An adaptive artificial neural network model for sizing stand-alone photovoltaic systems: application for isolated sites in Algeria. Renew Energy 30(10):1501–1524CrossRefGoogle Scholar
  55. 55.
    Mellit A, Benghanem M, Arab AH, Guessoum A (2003) Modeling of sizing the photovoltaic system parameters using artificial neural network. In: Proceedings of IEEE, Conference on Control Application, vol 1, pp 353–357Google Scholar
  56. 56.
    Mellit A, Kalogirou S (2008) Artificial intelligence techniques for photovoltaic applications: a review. Prog Energy Combust Sci 34:574–632CrossRefGoogle Scholar
  57. 57.
    Salah C, Lamamra K, Fatnassi A (2015) New optimally technical sizing procedure of domestic photovoltaic panel/battery system. J Renew Sustain Energy 7:1–14CrossRefGoogle Scholar
  58. 58.
    Mellit A (2010) ANN-based GA for generating the sizing curve of stand-alone photovoltaic systems. Adv Eng Softw 41:687–693CrossRefGoogle Scholar
  59. 59.
    Mellit A, Kalogirou S, Drif M (2010) Application of neural networks and genetic algorithms for sizing of photovoltaic systems. Renew Energy 35:2881–2893CrossRefGoogle Scholar
  60. 60.
    Khatib T, Ibrahim I, Mohamed A (2016) A review on sizing methodologies of photovoltaic array and storage battery in a standalone photovoltaic system. Energy Conversion and Management 120:430–448CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Rami Ghannam
    • 1
    Email author
  • Paulo Valente Klaine
    • 1
  • Muhammad Imran
    • 1
  1. 1.School of EngineeringUniversity of GlasgowGlasgowUK

Personalised recommendations