1. 1. S. Jawad and H. Zaidi, “Energy crisis and potential of solar energy in Pakistan,” vol. 31, pp. 194– 201, 2014.
   2. D. Bose and A. Bose, “Electrical Power Generation with Himalayan Mud Soil Using Microbial Fuel Key Words :,” Nat. Environ. Pollut. Technol., vol. 16, no. 2, pp. 433–439, 2017.
   3. A. Evans, V. Strezov, and T. J. Evans, “Assessment of sustainability indicators for renewable energy technologies,” Renew. Sustain. Energy Rev., vol. 13, no. 5, pp. 1082–1088, 2009.
   4. E. Dupont, R. Koppelaar, and H. Jeanmart, “Global available wind energy with physical and energy return on investment constraints,” Appl. Energy, vol. 209, pp. 322–338, 2018.
   5. S. K. Kurre, R. Garg, and S. Pandey, “A review of biofuel generated contamination, engine oil degradation and engine wear,” Biofuels, vol. 8, no. 2, pp. 273–280, 2017.
   6. D. Carder, R. Ryskamp, M. Besch, and A. Thiruvengadam, “Emissions Control Challenges for Compression Ignition Engines,” Procedia IUTAM, vol. 20, no. X, pp. 103–111, 2017. 
   7. C. K. and A. K. Srivastava, “Investigation on power aspects in impressed current cathodic protection system,” J. Corros. Sci. Eng., vol. 20, p. 10, 2017.
   8. W. K. Yap, T. Ho, and V. Karri, “Exhaust emissions control and engine parameters optimization using artificial neural network virtual sensors for a hydrogen-powered vehicle,” Int. J. Hydrogen Energy, vol. 37, no. 10, pp. 8704–8715, 2012.
   9. M. K. D. Kiani, B. Ghobadian, T. Tavakoli, A. M. Nikbakht, and G. Najafi, “Application of artificial neural networks for the prediction of performance and exhaust emissions in SI engine using ethanol- gasoline blends,” Energy, vol. 35, no. 1, pp. 65–69, 2010.
   10.  J. Mohammadhassani, A. Dadvand, S. Khalilarya, and M. Solimanpur, “Prediction and reduction of diesel engine emissions using a combined ANN-ACO method,” Appl. Soft Comput. J., vol. 34, pp. 139–150, 2015.
   11. S. Arumugam, G. Sriram, and P. R. Shankara Subramanian, “Application of artificial intelligence to predict the performance and exhaust emissions of diesel engine using rapeseed oil methyl ester,” Procedia Eng., vol. 38, pp. 853–860, 2012.
   12. J. M. J. M. J. M. J. M. J. M. Alonso et al., “Combining Neural Networks and Genetic Algorithms to Predict and Reduce Diesel Engine Emissions,” IEEE Trans. Evol. Comput., vol. 11, no. 1, pp. 46–55, 2007.
   13. E. ArcaklioÄŸlu and Ä°. Çelıkten, “A diesel engine’s performance and exhaust emissions,” Appl. Energy, vol. 80, no. 1, pp. 11–22, 2005.
   14. S. K. Jha, J. Bilalovic, A. Jha, N. Patel, and H. Zhang, “Renewable energy: Present research and future scope of Artificial Intelligence,” Renew. Sustain. Energy Rev., vol. 77, no. May 2016, pp. 297–317, 2017.
   15. M. C. Mabel and E. Fernandez, “Analysis of wind power generation and prediction using ANN: A case study,” Renew. Energy, vol. 33, no. 5, pp. 986–992, 2008.
   16. M. G. Simões, B. K. Bose, and R. J. Spiegel, “Design and performance evaluation of a fuzzy-logicbased variable-speed wind generation system,” IEEE Trans. Ind. Appl., vol. 33, no. 4, pp. 956–965, 1997.
   17. K. Philippopoulos and D. Deligiorgi, “Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography,” Renew. Energy, vol. 38, no. 1, pp. 75–82, 2012.
   18. F. S. Tymvios, C. P. Jacovides, S. C. Michaelides, and C. Scouteli, “Comparative study of Ångström’s and artificial neural networks’ methodologies in estimating global solar radiation,” Sol. Energy, vol. 78, no. 6, pp. 752–762, 2005
   19. J. Zeng and W. Qiao, “Short-term solar power prediction using a support vector machine,” Renew. Energy, vol. 52, pp. 118–127, 2013.
   20. D. M. Atia, F. H. Fahmy, N. M. Ahmed, and H. T. Dorrah, “Optimal sizing of a solar water heating system based on a genetic algorithm for an aquaculture system,” Math. Comput. Model., vol. 55, no. 3, pp. 1436– 1449, 2012.
   21. A. Bassam, E. Santoyo, J. Andaverde, J. A. Hernández, and O. M. Espinoza-Ojeda, “Estimation of static formation temperatures in geothermal wells by using an artificial neural network approach,” Comput. Geosci., vol. 36, no. 9, pp. 1191–1199, 2010. 
   22. P. K. S. Rathore, S. Rathore, R. Pratap Singh, and S. Agnihotri, “Solar power utility sector in india: Challenges and opportunities,” Renew. Sustain. Energy Rev., no. September 2016, pp. 1–11, 2017.
   23. O. R. Dolling and E. A. Varas, “Artificial neural networks for streamflow prediction,” J. Hydraul. Res., vol. 40, no. 5, pp. 547–554, 2002.
   24. S. N. Londhe and V. Panchang, “One-Day Wave Forecasts Based on Artificial Neural Networks,” J. Atmos. Ocean. Technol., vol. 23, no. 11, pp. 1593–1603, 2006.
   25. D. Bose, V. Kandpal, and H. Dhawan, “Energy Recovery with Microbial Fuel Cells : Bioremediation and Bioelectricity,” 2018.