Integration Of Renewable Hybrid Energy System: A State Of Art
Keywords:
Artificial Neural Networks (ANN); Photovoltaic system; Solar energy; Smart grid; Biodiesel energy; irrigation system; Wind energy; Renewable energy; Hybrid Energy Storage SystemAbstract
The presence of sunshine, air, and other resources on Earth must be used in a manner that promotes human well-being while safeguarding the environment and its inhabitants. The use of sunlight and air as a significant Renewable Energy (RE) source has been a critical area of innovation or new product development in recent years. But integrating AI and ML with renewable energy can be a breakthrough for the whole world. Artificial intelligence (AI) and machine learning (ML) have the potential to significantly contribute to the effectiveness, efficiency, and cost-cutting in the production of solar and biodiesel energy. The output of solar energy may be analyzed and expected based on weather patterns using ML algorithms, and the distribution of biodiesel fuels might benefit from AI's assistance in improving the supply chain. Artificial intelligence and machine learning will help the renewable energy industry expand, which will have a beneficial effect on the planet. So, the major focus of this paper is Artificial neural networks (ANN). The primary emphasis of the introductory section on ANNs in Renewable Energies is on their usage in Solar and Biodiesel. In the realm of Renewable Energies, ANNs have shown to be invaluable instruments for the prediction, control, and optimization of a wide range of systems. Advanced technologies like, Photovoltaic power prediction, maximum power point tracking, and optimum size of photovoltaic systems are just some of the ways that ANNs have been put to use in the Solar Energy sector. Artificial neural networks (ANNs) have been employed in the biodiesel industry for a variety of purposes, including the forecasting of fuel qualities, the improvement of the transesterification process, and the forecasting of engine performance. These examples show how ANNs may be put to good use in the Renewable Energy sector, where it can address a wide range of problems in an efficient and effective manner.
References
I. M. Shirbhate and S. S. Barve, “Solar panel monitoring and energy prediction for smart solar system,” Int. J. Adv. Appl. Sci., vol. 8, no. 2, p. 136, 2019, doi: 10.11591/ijaas.v8.i2.pp136-142.
S. Shakya, “A Self Monitoring and Analyzing System for Solar Power Station using IoT and Data Mining Algorithms,” J. Soft Comput. Paradig., vol. 3, no. 2, pp. 96–109, Jun. 2021, doi: 10.36548/jscp.2021.2.004.
R. K. Kodali and J. John, “Smart Monitoring of Solar Panels Using AWS,” in 2020 International Conference on Power Electronics and IoT Applications in Renewable Energy and its Control, PARC 2020, Feb. 2020, pp. 422–427. doi: 10.1109/PARC49193.2020.236645.
S. Padma and P. U. Ilavarasi, “Monitoring of Solar Energy using IOT,” Indian J. Emerg. Electron. Comput. Commun., vol. 4, no. 1, pp. 596–601, 2017.
M. N. A. Mohd Said, S. A. Jumaat, and C. R. A. Jawa, “Dual axis solar tracker with iot monitoring system using arduino,” Int. J. Power Electron. Drive Syst., vol. 11, no. 1, pp. 451–458, Mar. 2020, doi: 10.11591/ijpeds.v11.i1.pp451-458.
I. D. Hashim, A. A. Ismail, and M. A. Azizi, “Solar Tracker,” Int. J. Recent Technol. Appl. Sci., vol. 2, no. 1, pp. 59–65, Mar. 2020, doi: 10.36079/lamintang.ijortas-0201.60.
M. Ali, M. P.-I. J. E. A. S. Technol, and undefined 2020, “An IoT based approach for monitoring solar power consumption with Adafruit Cloud,” ijeast.com, vol. 4, pp. 335–341, 2020, Accessed: Jun. 29, 2022. [Online]. Available: http://www.ijeast.com/papers/335-341,Tesma409,IJEAST.pdf
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. April, pp. 297–317, 2017, doi: 10.1016/j.rser.2017.04.018.
P. C. Jena, H. Raheman, G. V. Prasanna Kumar, and R. Machavaram, “Biodiesel production from mixture of mahua and simarouba oils with high free fatty acids,” Biomass and Bioenergy, vol. 34, no. 8, pp. 1108–1116, 2010, doi: 10.1016/j.biombioe.2010.02.019.
L. P. N. Jyothy and M. R. Sindhu, “An Artificial Neural Network based MPPT Algorithm for Solar PV System,” Proc. 4th Int. Conf. Electr. Energy Syst. ICEES 2018, pp. 375–380, 2018, doi: 10.1109/ICEES.2018.8443277.
H. Qi, S.-C. Sun, Z.-Z. He, S.-T. Ruan, L.-M. Ruan, and H.-P. Tan, “Inverse Geometry Design of Radiative Enclosures Using Particle Swarm Optimization Algorithms,” Optim. Algorithms - Methods Appl., 2016, doi: 10.5772/62351.
O. Castillo, H. Neyoy, J. Soria, P. Melin, and F. Valdez, “A new approach for dynamic fuzzy logic parameter tuning in Ant Colony Optimization and its application in fuzzy control of a mobile robot,” Appl. Soft Comput. J., vol. 28, pp. 150–159, 2015, doi: 10.1016/j.asoc.2014.12.002.
A. Q. Mairizal et al., “Experimental study on the effects of feedstock on the properties of biodiesel using multiple linear regressions,” Renew. Energy, vol. 145, pp. 375–381, 2020, doi: 10.1016/j.renene.2019.06.067.
S. O. Giwa, S. O. Adekomaya, K. O. Adama, and M. O. Mukaila, “Prediction of selected biodiesel fuel properties using artificial neural network,” Front. Energy, vol. 9, no. 4, pp. 433–445, 2015, doi: 10.1007/s11708-015-0383-5.
T. F. Adepoju, B. E. Olatunbosun, O. M. Olatunji, and M. A. Ibeh, “Brette Pearl Spar Mable (BPSM): a potential recoverable catalyst as a renewable source of biodiesel from Thevetia peruviana seed oil for the benefit of sustainable development in West Africa,” Energy. Sustain. Soc., vol. 8, no. 1, pp. 1–17, 2018, doi: 10.1186/s13705-018-0164-1.
M. E. Borges, L. Hernández, J. C. Ruiz-Morales, P. F. Martín-Zarza, J. L. G. Fierro, and P. Esparza, “Use of 3D printing for biofuel production: efficient catalyst for sustainable biodiesel production from wastes,” Clean Technol. Environ. Policy, vol. 19, no. 8, pp. 2113–2127, 2017, doi: 10.1007/s10098-017-1399-9.
F. Ma and M. A. Hanna, “Biodiesel production: a review1Journal Series #12109, Agricultural Research Division, Institute of Agriculture and Natural Resources, University of Nebraska–Lincoln.1,” Bioresour. Technol., vol. 70, no. 1, pp. 1–15, 1999, doi: 10.1016/s0960-8524(99)00025-5.