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Machine Learning and Meta-heuristic Algorithms for Renewable Energy: A Systematic Review

  • Essam H. HousseinEmail author
Chapter
Part of the Power Systems book series (POWSYS)

Abstract

The demand for energy is become essential due to industrial activities and increasing agricultural of any nation. According to the aforementioned, the renewable energy resources available are very suitable to meet the ever-growing requirement of energy by the humanity rather causing any harmful effects to nature. Therefore, several research studies have been introduced in the renewable energy field such as solar, wind, biomass, and biogas due to the clean and sustainability. To better scheme and utilize this energy resource, good forecasting and optimization are necessary and intrinsic. So, this review introduces an overview of the renewable energy forecasting techniques that have been utilized in this field based on meta-heuristic optimization algorithms and machine learning (ML). In addition, several challenges have been addressed, recommendations for future research are provided, and a comprehensive bibliography is conducted. Eventually, in general speaking, this comprehensive review of renewable energy resources may help the researchers, energy planners, and policymakers.

References

  1. 1.
    Asif M, Muneer T (2007) Energy supply, its demand and security issues for developed and emerging economies. Renew Sustain Energy Rev 11(7):1388–1413CrossRefGoogle Scholar
  2. 2.
    Farooq M, Ramli A, Naeem A (2015) Biodiesel production from low FFA waste cooking oil using heterogeneous catalyst derived from chicken bones. Renew Energy 76:362–368CrossRefGoogle Scholar
  3. 3.
    Jebaraj S, Iniyan S (2006) A review of energy models. Renew Sustain Energy Rev 10(4):281–311CrossRefGoogle Scholar
  4. 4.
    Esen H et al (2008) Performance prediction of a ground-coupled heat pump system using artificial neural networks. Expert Syst Appl 35(4):1940–1948CrossRefGoogle Scholar
  5. 5.
    Kalogirou Soteris A (2006) Artificial neural networks in energy applications in buildings. Int J Low-Carbon Technol 1(3):201–216CrossRefGoogle Scholar
  6. 6.
    Kalogirou Soteris A (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 29(6):515–566CrossRefGoogle Scholar
  7. 7.
    Vapnik V (2013) The nature of statistical learning theory. Springer science & business mediaGoogle Scholar
  8. 8.
    Hamad A, Houssein EH, Hassanien AE, Fahmy AA, Bhattacharyya S (2018) A hybrid gray wolf optimization and support vector machines for detection of epileptic seizure. Series in machine perception and artificial intelligence, hybrid metaheuristics, pp 197–225Google Scholar
  9. 9.
    Hassanien AE, Kilany M, Houssein EH, AlQaheri H (2018) Intelligent human emotion recognition based on elephant herding optimization tuned support vector regression. Biomed Signal Process Control 45:182–191CrossRefGoogle Scholar
  10. 10.
    Ismail FH, Houssein EH, Hassanien AE (2018) Chaotic bird swarm optimization algorithm. In: International conference on advanced intelligent systems and informatics. Springer, ChamGoogle Scholar
  11. 11.
    Kaveh A, Bakhshpoori T (2016) Water evaporation optimization: a novel physically inspired optimization algorithm. Comput Struct 167:69–85CrossRefGoogle Scholar
  12. 12.
    Ebrahimi A, Khamehchi E (2016) Sperm whale algorithm: an effective metaheuristic algorithm for production optimization problems. J Nat Gas Sci Eng 29:211e222CrossRefGoogle Scholar
  13. 13.
    Seyedali Mirjalili SCA (2016) A sine cosine algorithm for solving optimization problems. Knowl Syst 96:120–133CrossRefGoogle Scholar
  14. 14.
    Zheng Yu-Jun (2015) Water wave optimization: a new nature-inspired metaheuristic. Comput Op Res 55:1–11MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Seyedali M (2016) Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput Appl 27(4):1053–1073Google Scholar
  16. 16.
    Wang G-G, Deb S, Coelho LDS Elephant herding optimization. In: 2015 3rd international symposium on computational and business intelligenceGoogle Scholar
  17. 17.
    Seyedali M, Mirjalili SM, Lewis A, Wolf G (2014) Optimizer. Adv Eng Softw 69:46–61Google Scholar
  18. 18.
    Sauber AM, Nasef MM, Houssein EH, Hassanien AE (2018) Parallel whale optimization algorithm for solving constrained and unconstrained optimization problems. arXiv:1807.09217
  19. 19.
    Hussien AG et al (2019) S-shaped binary whale optimization algorithm for feature selection. Recent Trends Signal Image Process. Springer, Singapore, pp 79–87Google Scholar
  20. 20.
    Hussien AG, Hassanien AE, Houssein EH (2017) Swarming behaviour of salps algorithm for predicting chemical compound activities. In: 2017 eighth international conference on intelligent computing and information systems (ICICIS), IEEEGoogle Scholar
  21. 21.
    Tharwat A et al (2017) MOGOA algorithm for constrained and unconstrained multi-objective optimization problems. Appl Intell 1–16Google Scholar
  22. 22.
    Ewees AA, Elaziz MA, Houssein EH (2018) Improved grasshopper optimization algorithm using opposition-based learning. Exp Syst ApplGoogle Scholar
  23. 23.
    Hassanien AE et al (2018) Intelligent human emotion recognition based on elephant herding optimization tuned support vector regression. Biomed Signal Process Control 45:182–191CrossRefGoogle Scholar
  24. 24.
    Houssein EH, Ewees AA, ElAziz MA (2018) Improving twin support vector machine based on hybrid swarm optimizer for heartbeat classification. Pattern Recognit Image Anal 28(2):243–253CrossRefGoogle Scholar
  25. 25.
    Gaurav D, Vijay K (2017) Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications. Adv Eng Softw 114:48–70Google Scholar
  26. 26.
    Salmani MH, Eshghi K (2017) A metaheuristic algorithm based on chemotherapy science: CSA. J OptGoogle Scholar
  27. 27.
    Long C, Wu X, Yan W (2018) Artificial flora (AF) optimization algorithm. Appl Sci 8(3):329Google Scholar
  28. 28.
    Gaurav D, Vijay K (2018) Emperor penguin optimizer: a bio-inspired algorithm for engineering problems. Knowl SystGoogle Scholar
  29. 29.
    Hamed SH (2009) The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. Int J Bio-Inspired Comput 1(1–2):71–79Google Scholar
  30. 30.
    Behzad J, Abdolreza H, Seyedali M (2015) Ions motion algorithm for solving optimization problems. Appl Soft Comput 32:72–79Google Scholar
  31. 31.
    Kilany M, Houssein EH, Hassanien AE, Badr A (2017) Hybrid water wave optimization and support vector machine to improve EMG signal classification for neurogenic disorders. In: 2017 12th international conference on computer engineering and systems (ICCES). IEEE, pp 686–691Google Scholar
  32. 32.
    Hussien AG, Houssein EH, Hassanien AE (2017) A binary whale optimization algorithm with hyperbolic tangent fitness function for feature selection. In: 2017 eighth international conference on intelligent computing and information systems (ICICIS), IEEEGoogle Scholar
  33. 33.
    Ahmed MM et al (2017) Maximizing lifetime of wireless sensor networks based on whale optimization algorithm. In: International Conference on Advanced Intelligent Systems and Informatics. Springer, ChamGoogle Scholar
  34. 34.
    Houssein EH, Wazery YM (2017) Vortex search topology control algorithm for wireless sensor networks. Int J Intell Eng Syst 10(6):87–97CrossRefGoogle Scholar
  35. 35.
    Bhaskar K, Singh SN (2012) AWNN-assisted wind power forecasting using feed-forward neural network. IEEE Trans Sustain Energy 3(2):306–315CrossRefGoogle Scholar
  36. 36.
    Shu F, Liao James R, Ryuichi Y, Luonan C, Wei-Jen L (2009) Forecasting the wind generation using a two-stage network based on meteorological information. IEEE Trans Energy Conv 24(2):474–482Google Scholar
  37. 37.
    Lew D, Milligan M, Jordan G, Freeman L, Miller N, Clark K, Piwko R (2009) How do wind and solar power affect grid operations: the western wind and solar integration study. In: 8th international workshop on large scale integration of wind power and on transmission networks for offshore wind farms, pp 14–15Google Scholar
  38. 38.
    Cameron P, Debra L, Jim M, Sam C, Scott E, Eric G (2008) Creating the dataset for the western wind and solar integration study (USA). Wind Eng 32(4):325–338Google Scholar
  39. 39.
    Yuanyuan F, Guijun Y, Jihua W, Xiaoyu S, Haikuan F (2014) Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Comput Electron Agric 100:51–59Google Scholar
  40. 40.
    Gnyp Martin L, Yuxin M, Fei Y, Ustin Susan L, Yu K, Yinkun Y, Shanyu H, Georg B (2014) Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crops Res 155:42–55CrossRefGoogle Scholar
  41. 41.
    Shuai G, Zheng N, Ni H, Xuehui H (2013) Estimating the leaf area index, height and biomass of maize using HJ-1 and RADARSAT-2. Int J Appl Earth Observ Geoinf 24:1–8Google Scholar
  42. 42.
    Xiuliang J, Guijun Y, Xu X, Hao Y, Haikuan F, Zhenhai L, Jiaxiao S, Yubin L, Chunjiang Z (2015) Combined multi-temporal optical and radar parameters for estimating LAI and biomass in winter wheat using HJ and RADARSAR-2 data. Remote Sens 7(10):13251–13272Google Scholar
  43. 43.
    Valentina B, Michela C, Giulia F, Elena V (2012) The future prospect of PV and CSP solar technologies: an expert elicitation survey. Energy Policy 49:308–317CrossRefGoogle Scholar
  44. 44.
    Klaus B, Philipp G, Luise H (2015) Comparing the incomparable: Lessons to be learned from models evaluating the feasibility of Desertec. Energy 82:905–913CrossRefGoogle Scholar
  45. 45.
    Iglesias G, Carballo R (2011) Wave resource in El Hierroan island towards energy self-sufficiency. Renew Energy 36(2):689–698CrossRefGoogle Scholar
  46. 46.
    Merino J, Veganzones C, Sanchez JA, Martinez S, Platero Carlos A (2012) Power system stability of a small sized isolated network supplied by a combined wind-pumped storage generation system: A case study in the Canary Islands. Energies 5(7):2351–2369CrossRefGoogle Scholar
  47. 47.
    Ashnani MH, Mohammadi AJ, Hashim H, Hasani E (2014) A source of renewable energy in Malaysia, why biodiesel? Renew Sustain Energy Rev 35:244–257CrossRefGoogle Scholar
  48. 48.
    Lee HV, Juan JC, Taufiq-Yap YH (2015) Preparation and application of binary acidbase CaOLa2 O3 catalyst for biodiesel production. Renew Energy 74:124–132CrossRefGoogle Scholar
  49. 49.
    BDO Deutsche Warentreuhand AG, Der Biogas market nach der EEG-Novelle (The biogas market after the Renewable Energy Sources Act amendment), p 89 (2008)Google Scholar
  50. 50.
    Koppe P, Stozek A (1993) Municipal wastewaterits ingredients according to origin, composition and reactions in waste water treatment processes including biosolid. Auflage, EssenGoogle Scholar
  51. 51.
    Batstone Damien J, Keller J, Angelidaki I, Kalyuzhnyi SV, Pavlostathis SG, Rozzi A, Sanders WTM, Siegrist H, Vavilin VA (2002) The IWA anaerobic digestion model no 1 (ADM1). Water Sci Technol 45(10):65–73CrossRefGoogle Scholar
  52. 52.
    Strik David PBTB, Domnanovich AM, Zani L, Braun R, Holubar P (2005) Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB neural network toolbox. Environ Model Softw 20(6):803–810CrossRefGoogle Scholar
  53. 53.
    Shamshirband S et al (2014) Survey of four models of probability density functions of wind speed and directions by adaptive neuro-fuzzy methodology. Adv Eng Softw (76):148–153CrossRefGoogle Scholar
  54. 54.
    Kolhe M, Lin TC, Maunuksela J (2011) GA-ANN for short-term wind energy prediction. In: Power and energy engineering conference (APPEEC), 2011 Asia-Pacific. IEEEGoogle Scholar
  55. 55.
    Peng H, Liu F, Yang X (2013) A hybrid strategy of short term wind power prediction. Renew Energy 50:590–595CrossRefGoogle Scholar
  56. 56.
    Kalogirou Soteris A (2000) Applications of artificial neural-networks for energy systems. Appl Energy 67(1):17–35CrossRefGoogle Scholar
  57. 57.
    Jursa R, Rohrig K (2008) Short-term wind power forecasting using evolutionary algorithms for the automated specification of artificial intelligence models. Int J Forecast 24(4):694–709CrossRefGoogle Scholar
  58. 58.
    Chen B et al (2009) Wind speed prediction using OLS algorithm based on RBF neural network. In: 2009 Asia-Pacific power and energy engineering conference, IEEEGoogle Scholar
  59. 59.
    Fonte PM, Silva GX, Quadrado JC (2005) Wind speed prediction using artificial neural networks. WSEAS Trans Syst 4(4):379–384Google Scholar
  60. 60.
    Li G, Shi J, Zhou J (2011) Bayesian adaptive combination of short-term wind speed forecasts from neural network models. Renew Energy 36(1):352–359CrossRefGoogle Scholar
  61. 61.
    Zhao P et al (2012) Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China. Renew Energy 43:234–241CrossRefGoogle Scholar
  62. 62.
    Ata R (2015) Artificial neural networks applications in wind energy systems: a review. Renew Sustain Energy Rev 49:534–562CrossRefGoogle Scholar
  63. 63.
    Mohandes M, Rehman S, Rahman SM (2011) Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Appl Energy 88(11):4024–4032CrossRefGoogle Scholar
  64. 64.
    Monfared M, Rastegar H, Kojabadi HM (2009) A new strategy for wind speed forecasting using artificial intelligent methods. Renew Energy 34(3):845–848CrossRefGoogle Scholar
  65. 65.
    Catalo JPS, Pousinho HMI, Mendes VMF (2009) An artificial neural network approach for short-term wind power forecasting in Portugal. In: 15th international conference on in intelligent system applications to power systems, ISAP ’09, pp 1–5Google Scholar
  66. 66.
    Gopi ES, Palanisamy P (2015) Neural network based class-conditional probability density function using kernel trick for supervised classifier. Neurocomputing 154:225–229CrossRefGoogle Scholar
  67. 67.
    An S, Shi H, Qinghua H, Li X, Dang Jianwu (2014) Fuzzy rough regression with application to wind speed prediction. Inf Sci 282:388–400MathSciNetCrossRefGoogle Scholar
  68. 68.
    Sheela KG, Deepa SN (2013) Neural network based hybrid computing model for wind speed prediction. Neurocomputing 122:425–429CrossRefGoogle Scholar
  69. 69.
    Liu Z, Gao W, Wan Y-H, Muljadi E (2012) Wind power plant prediction by using neural networks. In: 2012 IEEE energy conversion congress and exposition (ECCE), pp 3154–3160, IEEEGoogle Scholar
  70. 70.
    Prez EC, Algredo-Badillo I, Rodrguez VHG (2012) Performance analysis of ANFIS in short term wind speed prediction. IJCSI Int J Comput Sci Iss 9(3)Google Scholar
  71. 71.
    Wang J et al (2015) Short-term wind speed forecasting using support vector regression optimized by cuckoo optimization algorithm. Math Probl EngGoogle Scholar
  72. 72.
    Santamaría-Bonfil G, Reyes-Ballesteros A, Gershenson C (2016) Wind speed forecasting for wind farms: a method based on support vector regression. Renew Energy 85:790–809CrossRefGoogle Scholar
  73. 73.
    Salcedo-Sanz S et al (2011) Short term wind speed prediction based on evolutionary support vector regression algorithms. Exp Syst Appl 38(4):4052–4057CrossRefGoogle Scholar
  74. 74.
    Chen X et al (2015) Short-term wind speed forecasting study and its application using a hybrid model optimized by cuckoo search. Math Probl EngGoogle Scholar
  75. 75.
    Koivisto M, Seppnen J, Mellin I, Ekstrm J, Millar John, Mammarella Ivan, Komppula Mika, Lehtonen Matti (2016) Wind speed modeling using a vector autoregressive process with a time-dependent intercept term. Int J Electr Power Energy Syst 77:91–99CrossRefGoogle Scholar
  76. 76.
    Catalo JPS, Pousinho HMI, Mendes VMF (2009) An artificial neural network approach for short-term wind power forecasting in Portugal. In: 15th international conference on intelligent system applications to power systems, 2009. ISAP ’09, IEEEGoogle Scholar
  77. 77.
    Gopi ES, Palanisamy P (2015) Neural network based class-conditional probability density function using kernel trick for supervised classifier. Neurocomputing 154:225–229CrossRefGoogle Scholar
  78. 78.
    Sheela KG, Deepa SN (2013) Neural network based hybrid computing model for wind speed prediction. Neurocomputing 122:425–429CrossRefGoogle Scholar
  79. 79.
    Liu Z et al (2012) Wind power plant prediction by using neural networks. In: Energy conversion congress and exposition (ECCE), IEEEGoogle Scholar
  80. 80.
    Wu Q (2010) A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization. Exp Syst Appl 37(3):2388–2394CrossRefGoogle Scholar
  81. 81.
    Ren C et al (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl Syst 56:226–239CrossRefGoogle Scholar
  82. 82.
    Douak F, Melgani F, Benoudjit N (2013) Kernel ridge regression with active learning for wind speed prediction. Appl energy 103:328–340CrossRefGoogle Scholar
  83. 83.
    De Giorgi M et al (2014) Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN). Energies 7(8):5251–5272CrossRefGoogle Scholar
  84. 84.
    Potonik P, Strmnik E, Govekar E (2015) Linear and neural network-based models for short-term heat load forecasting. Strojniki vestnik-J Mech Eng 61(9):543–550CrossRefGoogle Scholar
  85. 85.
    Azad HB, Mekhilef S, Ganapathy VG (2014) Long-term wind speed forecasting and general pattern recognition using neural networks. IEEE Trans Sustain Energy 5(2):546–553CrossRefGoogle Scholar
  86. 86.
    Kusiak A, Zheng H, Song Z (2009) Short-term prediction of wind farm power: a data mining approach. IEEE Trans Energy Conv 24(1):125–136CrossRefGoogle Scholar
  87. 87.
    Osama S, Darwish A, Houssein EH, Hassanien AE, Fahmy AA, Mahrous A (2017) Long-term wind speed prediction based on optimized support vector regression. In: 2017 eighth international conference on intelligent computing and information systems (ICICIS), pp 191–196, IEEEGoogle Scholar
  88. 88.
    Monfared M, Rastegar H, Kojabadi HM (2009) A new strategy for wind speed forecasting using artificial intelligent methods. Renew energy 34(3):845–848CrossRefGoogle Scholar
  89. 89.
    Osama S, Houssein EH, Hassanien AE, Fahmy AY (2017) Forecast of wind speed based on whale optimization algorithm. In: Proceedings of the 1st international conference on internet of things and machine learning, ACM, p 62Google Scholar
  90. 90.
    Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2016) Feature extraction of epilepsy EEG using discrete wavelet transform. In: Computer engineering conference (ICENCO), 2016 12th international, IEEE, pp 190–195Google Scholar
  91. 91.
    Guo Z, Chi D, Jie W, Zhang W (2014) A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm. Energy Conv Manag 84:140–151CrossRefGoogle Scholar
  92. 92.
    Sallis PJ, Claster W, Hernndez S (2011) A machine-learning algorithm for wind gust prediction. Comput Geosci 37(9):1337–1344CrossRefGoogle Scholar
  93. 93.
    Douak F, Melgani F, Benoudjit N (2013) Kernel ridge regression with active learning for wind speed prediction. Appl Energy 103:328–340CrossRefGoogle Scholar
  94. 94.
    Huang C-L, Dun J-F (2008) A distributed PSOSVM hybrid system with feature selection and parameter optimization. Appl Soft Comput 8(4):1381–1391CrossRefGoogle Scholar
  95. 95.
    Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2017) A hybrid EEG signals classification approach based on grey wolf optimizer enhanced SVMs for epileptic detection. In: International conference on advanced intelligent systems and informatics. Springer, Cham, pp 108–117Google Scholar
  96. 96.
    Hamad A, Houssein EH, Hassanien AE, Fahmy AA (2018) Hybrid grasshopper optimization algorithm and support vector machines for automatic seizure detection in EEG signals. In: International conference on advanced machine learning technologies and applications. Springer, Cham, pp 82–91CrossRefGoogle Scholar
  97. 97.
    Osama S, Houssein EH, Hassanien AE, Fahmy AA (2017) Forecast of wind speed based on whale optimization algorithm. In: International conference on internet of things and machine learning (IML, 2017) Liverpool city. ACM, United KingdomGoogle Scholar
  98. 98.
    Osama S, Houssein EH, Darwish A, Hassanien AE, Fahmy AA (2018) An optimized support vector regression using whale optimization for long term wind speed forecasting. Series in machine perception and artificial intelligence, hybrid metaheuristics, pp 171–196Google Scholar
  99. 99.
    Ren C, An N, Wang J, Li L, Bin Hu, Shang Duo (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl Syst 56:226–239CrossRefGoogle Scholar
  100. 100.
    Jiang Y, Song Z, Kusiak A (2013) Very short-term wind speed forecasting with Bayesian structural break model. Renew Energy 50:637–647CrossRefGoogle Scholar
  101. 101.
    Fei S, He Y (2015) Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine. Int J Electr Power Energy Syst 73:625–631CrossRefGoogle Scholar
  102. 102.
    Wu Q (2010) A hybrid-forecasting model based on Gaussian support vector machine and chaotic particle swarm optimization. Exp Syst Appl 37(3):2388–2394CrossRefGoogle Scholar
  103. 103.
    Carneiro TC, Melo SP, Carvalho PCM, Braga APS (2016) Particle swarm optimization method for estimation of Weibull parameters: a case study for the Brazilian northeast region. Renew Energy 86:751–759CrossRefGoogle Scholar
  104. 104.
    Houssein EH (2017) Particle swarm optimization-enhanced twin support vector regression for wind speed forecasting. J Intell Syst De GruyterGoogle Scholar
  105. 105.
    Fei S, He Y (2015) Wind speed prediction using the hybrid model of wavelet decomposition and artificial bee colony algorithm-based relevance vector machine. Int J Electr Power Energy Syst 73:625–631CrossRefGoogle Scholar
  106. 106.
    Mahto T, Mukherjee V (2016) Evolutionary optimization technique for comparative analysis of different classical controllers for an isolated winddiesel hybrid power system. Swarm Evolut Comput 26:120–136Google Scholar
  107. 107.
    Soon JJ, Low K-S (2012) Optimizing photovoltaic model parameters for simulation. In: IEEE international industrial electronics (ISIE)Google Scholar
  108. 108.
    Azab M (2012) Optimal power point tracking for stand-alone PV system using particle swarm optimization. Int J Renew Energy TechnolGoogle Scholar
  109. 109.
    Ishaque K, Salam Z, Amjad M, Mekhilef S (2012) An improved particle swarm optimization (PSO) based MPPT for PV with reduced steady-state oscillation. IEEE Trans Power Electron 27Google Scholar
  110. 110.
    Tumbelaka HH, Miyatake M (2010) A grid current-controlled inverter with particle swarm optimization MPPT for PV generators. World Acad Sci Eng Technol 43Google Scholar
  111. 111.
    Fu Q, Tong N (2010) A new PSO algorithm based on adaptive grouping for photovoltaic MPP prediction. In: International workshop on intelligent systems and applications, ChinaGoogle Scholar
  112. 112.
    Boutasseta N (2012) PSO-PI based control of photovoltaic arrays. Int J Comput ApplGoogle Scholar
  113. 113.
    Ngan MS, Tan CW (2011) Multiple peaks tracking algorithm using particle swarm optimization incorporated with artificial neural network. World Acad Sci Eng Technol 58Google Scholar
  114. 114.
    Wang L, Zhou X, Zhu X, Dong Z, Guo Wenshan (2016) Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. Crop J 4(3):212–219CrossRefGoogle Scholar
  115. 115.
    Lpez PR, Jurado F, Ruiz Reyes N, Garca Galn S, Gmez M (2008) Particle swarm optimization for biomass-fuelled systems with technical constraints. Eng Appl Artif Intell 21(8):1389–1396Google Scholar
  116. 116.
    Izquierdo J, Minciardi R, Montalvo I, Robba M, Tavera M (2008) Particle swarm optimization for the biomass supply chain strategic planning. In: Proceedings of the international congress on environmental modelling and software, pp 1272–1280Google Scholar
  117. 117.
    Sedighizadeh M, Rafiei M, Hakimi A (2013) Optimizing a typical biomass fueled power plant using genetic algorithm and binary particle swarm optimization. Int J Tech Phys Probl Eng 5:15–21Google Scholar
  118. 118.
    Chen X, Bin X, Mei C, Ding Yuhan, Li Kangji (2018) Teaching learning based artificial bee colony for solar photovoltaic parameter estimation. Appl Energy 212:1578–1588CrossRefGoogle Scholar
  119. 119.
    Polo J, Tllez FM, Tapia C (2016) Comparative analysis of long-term solar resource and CSP production for bankability. Renew Energy 90:38–45CrossRefGoogle Scholar
  120. 120.
    Rezvani A, Gandomkar M (2017) Simulation and control of intelligent photovoltaic system using new hybrid fuzzy-neural method. Neural Comput Appl 28(9):2501–2518CrossRefGoogle Scholar
  121. 121.
    Olatomiwa L et al (2015) A support vector machine firefly algorithm-based model for global solar radiation prediction. Solar Energy 115:632–644CrossRefGoogle Scholar
  122. 122.
    Munshi AA, Mohamed Yasser A-RI (2017) Comparisons among Bat algorithms with various objective functions on grouping photovoltaic power patterns. Solar Energy 144:254–266CrossRefGoogle Scholar
  123. 123.
    Et-torabi K et al (2017) Parameters estimation of the single and double diode photovoltaic models using a GaussSeidel algorithm and analytical method: a comparative study. Energy Conv Manage 148:1041–1054CrossRefGoogle Scholar
  124. 124.
    Tong NT, Pora W (2016) A parameter extraction technique exploiting intrinsic properties of solar cells. Appl Energy 176:104–115CrossRefGoogle Scholar
  125. 125.
    Patel SJ, Panchal AK, Kheraj V (2014) Extraction of solar cell parameters from a single currentvoltage characteristic using teaching learning based optimization algorithm. Appl Energy 119:384–393CrossRefGoogle Scholar
  126. 126.
    Prasanth RJ, Sudhakar Babu T, Rajasekar N (2017) A comprehensive review on solar PV maximum power point tracking techniques. Renew Sustain Energy Rev 67:826–847Google Scholar
  127. 127.
    Nassar-Eddine I et al (2016) Parameter estimation of photovoltaic modules using iterative method and the Lambert W function: a comparative study. Energy Conv Manage 119:37–48CrossRefGoogle Scholar
  128. 128.
    Gao X et al (2018) Parameter extraction of solar cell models using improved shuffled complex evolution algorithm. Energy Conv Manage 157:460–479CrossRefGoogle Scholar
  129. 129.
    Babu TS et al (2016) Parameter extraction of two diode solar PV model using fireworks algorithm. Solar Energy 140:265–276CrossRefGoogle Scholar
  130. 130.
    Guo L et al (2016) Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm. Energy Conv Manage 108:520–528CrossRefGoogle Scholar
  131. 131.
    Chen X et al (2016) Parameters identification of solar cell models using generalized oppositional teaching learning based optimization. Energy 99:170–180CrossRefGoogle Scholar
  132. 132.
    Allam D, Yousri DA, Eteiba MB (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell/module using Moth-Flame optimization algorithm. Energy Conv Manage 123:535–548CrossRefGoogle Scholar
  133. 133.
    Saad NH, El-Sattar AA, Mansour AE-AM (2016) Improved particle swarm optimization for photovoltaic system connected to the grid with low voltage ride through capability. Renew Energy 85:181–194CrossRefGoogle Scholar
  134. 134.
    Fathy A, Rezk H (2017) Parameter estimation of photovoltaic system using imperialist competitive algorithm. Renew Energy 111:307–320CrossRefGoogle Scholar
  135. 135.
    Ali EE et al (2016) Parameter extraction of photovoltaic generating units using multi-verse optimizer. Sustain Energy Technol Assess 17:68–76Google Scholar
  136. 136.
    Li W et al (2017) A coupled optical-thermal-electrical model to predict the performance of hybrid PV/T-CCPC roof-top systems. Renew Energy 112:166–186CrossRefGoogle Scholar
  137. 137.
    Chin VJ, Salam Z, Ishaque K (2016) An accurate modelling of the two-diode model of PV module using a hybrid solution based on differential evolution. Energy Conv Manage 124:42–50CrossRefGoogle Scholar
  138. 138.
    Louzazni M et al (2018) Metaheuristic algorithm for photovoltaic parameters: comparative study and prediction with a firefly algorithm. Appl Sci 8(3):339CrossRefGoogle Scholar
  139. 139.
    Derick M et al (2017) An improved optimization technique for estimation of solar photovoltaic parameters. Solar Energy 157:116–124CrossRefGoogle Scholar
  140. 140.
    Abbassi A et al (2017) An improved single-diode model parameters extraction at different operating conditions with a view to modeling a photovoltaic generator: a comparative study. Solar Energy 155:478–489CrossRefGoogle Scholar
  141. 141.
    Elena CL, Saul PO, Hernandez M, Bandarra FEP (2017) Comparison of four methods for parameter estimation of mono-and multi-junction photovoltaic devices using experimental data. Renew Sustain Energy RevGoogle Scholar
  142. 142.
    Baig H et al (2018) Conceptual design and performance evaluation of a hybrid concentrating photovoltaic system in preparation for energy. Energy 147:547–560CrossRefGoogle Scholar
  143. 143.
    Jordehi AR (2018) Enhanced leader particle swarm optimisation (ELPSO): an efficient algorithm for parameter estimation of photovoltaic (PV) cells and modules. Solar Energy 159:78–87CrossRefGoogle Scholar
  144. 144.
    Xu S, Wang Y (2017) Parameter estimation of photovoltaic modules using a hybrid flower pollination algorithm. Energy Conv Manage 144:53–68CrossRefGoogle Scholar
  145. 145.
    Lin P et al (2017) Parameters extraction of solar cell models using a modified simplified swarm optimization algorithm. Solar Energy 144:594–603CrossRefGoogle Scholar
  146. 146.
    Kler D et al (2017) PV cell and module efficient parameters estimation using evaporation rate based water cycle algorithm. Swarm Evolut Comput 35:93–110CrossRefGoogle Scholar
  147. 147.
    Barth N et al (2016) PV panel single and double diode models: optimization of the parameters and temperature dependence. Solar Energy Mater Solar Cells 148:87–98CrossRefGoogle Scholar
  148. 148.
    Li W et al (2016) Six-parameter electrical model for photovoltaic cell/module with compound parabolic concentrator. Solar Energy 137:551–563CrossRefGoogle Scholar
  149. 149.
    Rohit AK et al (2017) Virtual lab based real-time data acquisition, measurement and monitoring platform for solar photovoltaic module. Res Eff Technol 3(4):446–451CrossRefGoogle Scholar
  150. 150.
    Cotfas DT, Cotfas PA, Kaplanis S (2016) Methods and techniques to determine the dynamic parameters of solar cells. Renew Sustain Energy Rev 61:213–221CrossRefGoogle Scholar
  151. 151.
    Jordehi AR (2016) Parameter estimation of solar photovoltaic (PV) cells: a review. Renew Sustain Energy Rev 61:354–371CrossRefGoogle Scholar
  152. 152.
    Humada AM et al (2016) Solar cell parameters extraction based on single and double-diode models: a review. Renew Sustain Energy Rev 56:494–509CrossRefGoogle Scholar
  153. 153.
    Yu K, Chen X, Wang X, Wang Z (2017) Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conv Manage 145:233–246CrossRefGoogle Scholar
  154. 154.
    Alam DF, Yousri DA, Eteiba MB (2015) Flower pollination algorithm based solar PV parameter estimation. Energy Conv Manage 101:410–422CrossRefGoogle Scholar
  155. 155.
    Oliva D, El Aziz MA, Hassanien AE (2017) Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm. Appl Energy 200:141–154CrossRefGoogle Scholar
  156. 156.
    Yu K et al (2017) Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization. Energy Conv Manage 145:233–246CrossRefGoogle Scholar
  157. 157.
    Attia A-F, El Sehiemy RA, Hasanien HM (2018) Optimal power flow solution in power systems using a novel Sine-Cosine algorithm. Int J Electric Power Energy Syst 99:331–343CrossRefGoogle Scholar
  158. 158.
    Prasanth RJ, Rajasekar N (2017) A new global maximum power point tracking technique for solar photovoltaic (PV) system under partial shading conditions (PSC). Energy 118:512–525CrossRefGoogle Scholar
  159. 159.
    Titri S et al (2017) A new MPPT controller based on the Ant colony optimization algorithm for photovoltaic systems under partial shading conditions. Appl Soft Comput 58:465–479CrossRefGoogle Scholar
  160. 160.
    Chaieb H, Sakly Anis (2018) A novel MPPT method for photovoltaic application under partial shaded conditions. Solar Energy 159:291–299CrossRefGoogle Scholar
  161. 161.
    Mirhassani SM et al (2015) An improved particle swarm optimization based maximum power point tracking strategy with variable sampling time. Int J Electric Power Energy Syst 64:761–770CrossRefGoogle Scholar
  162. 162.
    Soufyane Benyoucef A et al (2015) Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions. Appl Soft Comput 32:38–48CrossRefGoogle Scholar
  163. 163.
    Diab AAZ, Rezk H (2017) Global MPPT based on flower pollination and differential evolution algorithms to mitigate partial shading in building integrated PV system. Solar Energy 157:171–186CrossRefGoogle Scholar
  164. 164.
    Chao K-H, Lin Y-S, Lai U-D (2015) Improved particle swarm optimization for maximum power point tracking in photovoltaic module arrays. Appl Energy 158:609–618CrossRefGoogle Scholar
  165. 165.
    Babu TS, Rajasekar N, Sangeetha K (2015) Modified particle swarm optimization technique based maximum power point tracking for uniform and under partial shading condition. Appl Soft Comput 34:613–624Google Scholar
  166. 166.
    Shi J, Zhang W, Zhang Y, Xue F, Yang T (2015) MPPT for PV systems based on a dormant PSO algorithm. Electric Power Syst Res 123:100–107CrossRefGoogle Scholar
  167. 167.
    Rezk H, Fathy A, Abdelaziz AY (2017) A comparison of different global MPPT techniques based on meta-heuristic algorithms for photovoltaic system subjected to partial shading conditions. Renew Sustain Energy Rev 74:377–386CrossRefGoogle Scholar
  168. 168.
    Ahmed J, Salam Z (2014) A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability. Appl Energy 119:118–130CrossRefGoogle Scholar
  169. 169.
    Askarzadeh A, Rezazadeh A (2013) Extraction of maximum power point in solar cells using bird mating optimizer-based parameters identification approach. Solar Energy 90:123–133CrossRefGoogle Scholar
  170. 170.
    Jiang P, Li X, Ruina X, Zhang F (2016) Heat extraction of novel underground well pattern systems for geothermal energy exploitation. Renew Energy 90:83–94CrossRefGoogle Scholar
  171. 171.
    Martnez-Lucas G, Sarasa JI, Snchez-Fernndez J, Wilhelmi JR (2016) Frequency control support of a wind-solar isolated system by a hydropower plant with long tail-race tunnel. Renew Energy 90:362–376Google Scholar
  172. 172.
    Boubaker K, Colantoni A, Marucci A, Longo L, Gambella Filippo, Cividino Sirio, Gallucci Francesco, Monarca Danilo, Cecchini Massimo (2016) Perspective and potential of CO\({}_2\): a focus on potentials for renewable energy conversion in the Mediterranean basin. Renew Energy 90:248–256CrossRefGoogle Scholar
  173. 173.
    Alves JCL, Henriques CB, Poppi RJ (2012) Determination of diesel quality parameters using support vector regression and near infrared spectroscopy for an in-line blending optimizer system. Fuel 97:710–717CrossRefGoogle Scholar
  174. 174.
    Wolf C, McLoone S, Bongards M (2008) Biogas plant optimization using genetic algorithms and particle swarm optimization, pp 244–249Google Scholar
  175. 175.
    Gsanger S, Pitteloud Jean-Daniel (2012) World wind energy report 2011. World Wind Energy Association, Bonn, GermanyGoogle Scholar
  176. 176.
    Naam R (2013) The infinite resource: the power of ideas on a finite planet, UPNEGoogle Scholar
  177. 177.
    Hardcastle JL Mining industry slow to adopt renewable energy tech, 28 Oct 2013. http://www.energymanagertoday.com/renewables-in-the-mining-industry-draft-096453/

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Faculty of Computers and InformationMinia UniversityMinyaEgypt

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