(University of Kermanshah, Iran, Islamic Republic of)(2) Willis Agutu
(Texas Tech University, United States)(3) * Ali Rezaei
(Quchan University of Technology, Iran, Islamic Republic of)(4) Nima Pourmaleki
(Sanandaj Azad University, Iran, Islamic Republic of)*corresponding author
AbstractRenewable energy sources are expected to replace traditional energy sources such as oil and gas in the future. It goes without saying that solar energy has been demonstrated to be a key source of green energy. Solar energy is used because it is abundant, pollution-free, and easily available. However, the power utility market requires highly exact solar energy forecasts. These challenges need the creation of a device that can precisely predict solar energy output via processing the location's weather data, which is accomplished through the use of machine learning and multiple linear regression (MLR). Some elements, such as the number of cloudy days, humidity, temperature, wind condition, and precipitation, should be addressed while simulating solar power output. In this paper, a 12-kW photovoltaic (PV) system on the rooftop of a house in Isfahan was studied using the System Advisor Model (SAM). The most significant research contribution of the proposed paper is to predict the output power of a solar system with the lowest possible error. According to the simulation results, by using the MLR model, the predicted power has an error of 6 % with the actual power, which is a very good estimation. In addition, this system meets each household's energy needs plus an additional 8430 kWh per year, resulting in being paid by utility companies, a fewer number of outages, and lower air pollution levels.
KeywordsMachine learning SAM Solar energy Pollution free Renewable energy
|
DOIhttps://doi.org/10.31763/ijrcs.v2i2.702 |
Article metrics10.31763/ijrcs.v2i2.702 Abstract views : 1367 | PDF views : 437 |
Cite |
Full Text Download
|
References
[1] F. Chien, M. Sadiq, M. A. Nawaz, M. S. Hussain, T. D. Tran, and T. L. Thanh, "A step toward reducing air pollution in top Asian economies: The role of green energy, eco-innovation, and environmental taxes," Journal of environmental management, vol. 297, p. 113420, 2021, https://doi.org/10.1016/j.jenvman.2021.113420.
[2] G. Luderer, S. Madeddu, L. Merfort, F. Ueckerdt, M. Pehl, R. Pietzcker, M. Rottoli, F. Schreyer, N. Bauer, L. Baumstark, C. Bertram, A. Dirnaichner, F. Humpenöder, A. Levesque, A. Popp, R. Rodrigues, J. Strefler, and E. Kriegler, "Impact of declining renewable energy costs on electrification in low-emission scenarios," Nature Energy, vol. 7, no. 1, pp. 32-42, 2022, https://doi.org/10.1038/s41560-021-00937-z.
[3] Q. Wang, Z. Dong, R. Li, and L. Wang, "Renewable energy and economic growth: new insight from country risks," Energy, vol. 238, p. 122018, 2022, https://doi.org/10.1016/j.energy.2021.122018.
[4] B. Kroposki, B. Johnson, Y. Zhang, V. Gevorgian, P. Denholm, B.-M. Hodge, and B. Hannegan, "Achieving a 100% renewable grid: Operating electric power systems with extremely high levels of variable renewable energy," IEEE Power and energy magazine, vol. 15, no. 2, p. 61-73, 2017, https://doi.org/10.1109/MPE.2016.2637122.
[5] A. Balal and F. Shahabi, "Ltspice Analysis of Double-Inductor Quadratic Boost Converter in Comparison with Quadratic Boost and Double Cascaded Boost Converter," 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1-6, 2021, https://doi.org/10.1109/ICCCNT51525.2021.9579931.
[6] R. L. Fares and M. E. Webber, "The impacts of storing solar energy in the home to reduce reliance on the utility," Nature Energy, vol. 2, no. 2, pp. 1-10, 2017, https://doi.org/10.1038/nenergy.2017.1.
[7] U. Bulut and A. Menegaki, "Solar energy-economic growth nexus in top 10 countries with the highest installed capacity," Energy Sources, Part B: Economics, Planning, and Policy, vol. 15, no. 5, pp. 297-310, 2020, https://doi.org/10.1080/15567249.2020.1788192.
[8] Z. Ramedani, M. Omid, A. Keyhani, B. Khoshnevisan, and Hadi Saboohi, "A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran," Solar Energy, vol. 109, pp. 135-143, 2014, https://doi.org/10.1016/j.solener.2014.08.023.
[9] H. Sarper, I. Melnykov, and L. A. Martínez, "Prediction of Daily Photovoltaic Energy Production Using Weather Data and Regression," Journal of Solar Energy Engineering, vol. 143, no. 6, 2021, https://doi.org/10.1115/1.4051262
[10] O. Abedinia, N. Amjady, and N. Ghadimi, "Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm," Computational Intelligence, vol. 34, no. 1, pp. 241-260, 2018, https://doi.org/10.1111/coin.12145.
[11] Z. Qadir, S. I. Khan, E. Khalaji, H. S. Munawar, F. Al-Turjman, M. A. P. Mahmud, A. Z. Kouzani, and K. Le, "Predicting the energy output of hybrid PV–wind renewable energy system using feature selection technique for smart grids," Energy Reports, vol. 7, pp. 8465-8475, 2021, https://doi.org/10.1016/j.egyr.2021.01.018.
[12] Y. S. Kim, H. Y. Joo, J. W. Kim, S. Y. Jeong, and J. H. Moon, "Use of a big data analysis in regression of solar power generation on meteorological variables for a Korean solar power plant," Applied Sciences, vol. 11, no. 4, p. 1776, 2021, https://doi.org/10.3390/app11041776.
[13] E. Paulescu and R. Blaga, "Regression models for hourly diffuse solar radiation," Solar Energy, vol. 125, pp. 111-124, 2016, https://doi.org/10.1016/j.solener.2015.11.044.
[14] K. Chiteka, R. Arora, and S. Sridhara, "A method to predict solar photovoltaic soiling using artificial neural networks and multiple linear regression models," Energy Systems, vol. 11, no. 4, pp. 981-1002, 2020, https://doi.org/10.1007/s12667-019-00348-w.
[15] H. Suyono, R. N. Hasanah, R. A. Setyawan, P. Mudjirahardjo, A. Wijoyo, and I. Musirin, "Comparison of solar radiation intensity forecasting using ANFIS and multiple linear regression methods," Bulletin of Electrical Engineering and Informatics, vol. 7, no. 2, pp. 191-198, 2018, https://doi.org/10.11591/eei.v7i2.1178.
[16] S. I. Bangdiwala, "Regression: multiple linear," International journal of injury control and safety promotion, vol. 25, no. 2, pp. 232-236, 2018 https://doi.org/10.1080/17457300.2018.1452336.
[17] A. Balal, M. Herrera, E. Johnson, and T. Dallas, "Design and Simulation of a Solar PV System for a University Building," 2021 IEEE 4th International Conference on Power and Energy Applications (ICPEA), 2021, https://doi.org/10.1109/ICPEA52760.2021.9639361.
[18] M. Alam, M. A. Dewan, S. S. Bashar, M. S. Miah, and A. Ghosh, "A Microcontroller Based Dual Axis Tracking System for Solar Panel," 2019 3rd International Conference on Electrical, Computer & Telecommunication Engineering (ICECTE), 2019, https://doi.org/10.1109/ICECTE48615.2019.9303534.
[19] S. Aziz and S. Hassan, "On improving the efficiency of a solar panel tracking system," Procedia Manufacturing, vol. 7, pp. 218-224, 2017, https://doi.org/10.1016/j.promfg.2016.12.053.
[20] S.-G. Kim, J.-Y. Jung, and M. K. Sim, "A two-step approach to solar power generation prediction based on weather data using machine learning," Sustainability, vol. 11, no. 5, p. 1501, 2019, https://doi.org/10.3390/su11051501.
[21] T. Demirdelen, I. O. Aksu, B. Esenboga, K. Aygul, F. Ekinci, and M. Bilgili, "A new method for generating short-term power forecasting based on artificial neural networks and optimization methods for solar photovoltaic power plants," Solar photovoltaic power plants, pp. 165-189, 2019, https://doi.org/10.1007/978-981-13-6151-7_8.
[22] M. Abuella and B. Chowdhury, "Solar power forecasting using artificial neural networks," 2015 North American Power Symposium (NAPS), 2015, https://doi.org/10.1109/NAPS.2015.7335176.
[23] D. Van Tai, "Solar photovoltaic power output forecasting using machine learning technique," Journal of Physics: Conference Series, 2019, https://doi.org/10.1088/1742-6596/1327/1/012051.
[24] P. Lauret, M. David, and H.T. Pedro, "Probabilistic solar forecasting using quantile regression models," energies, vol. 10, no. 10, p. 1591, 2017 https://doi.org/10.3390/en10101591.
[25] A. Balal, S. Dinkhah, F. Shahabi, M. Herrera, and Y. L. Chuang, "A Review on Multilevel Inverter Topologies," Emerging Science Journal, vol. 6, no. 1, pp. 185-200, 2022, https://doi.org/10.28991/ESJ-2022-06-01-014.
[26] S. A. Lopa, S. Hossain, M. K. Hasan, and T. K. Chakraborty, "Design and simulation of DC-DC converters," International Research Journal of Engineering and Technology (IRJET), vol. 3, no. 1, p. 63-70, 2016.
[27] A. Balal and M. Giesselmann, "Demand Side Management and Economic Analysis Using Battery Storage System (BSS) and Solar Energy," 2021 IEEE 4th International Conference on Power and Energy Applications (ICPEA), 2021, https://doi.org/10.1109/ICPEA52760.2021.9639359.
[28] A. Balal, M. Abedi, and F. Shahabi, "Optimized generated power of a solar PV system using an intelligent tracking technique," International Journal of Power Electronics and Drive Systems, vol. 12, no. 4, p. 2580, 2021, https://doi.org/10.11591/ijpeds.v12.i4.pp2580-2592.
[29] M. A. Eltawil and Z. Zhao, "MPPT techniques for photovoltaic applications," Renewable and sustainable energy reviews, vol. 25, p. 793-813, 2013, https://doi.org/10.1016/j.rser.2013.05.022.
[30] A. Ali, K. Almutairi, M. Z. Malik, K. Irshad, V. Tirth, S. Algarni, Md. H. Zahir, S. Islam, Md Shafiullah, and N. K. Shukla, "Review of online and soft computing maximum power point tracking techniques under non-uniform solar irradiation conditions," Energies, vol. 13, no. 12, p. 3256, 2020, https://doi.org/10.3390/en13123256
[31] S. Aslam, H. Herodotou, S. M. Mohsin, N. Javaid, N. Ashraf, and S. Aslam, "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, vol. 144, p. 110992, 2021, https://doi.org/10.1016/j.rser.2021.110992
[32] G. Muhammed and N. Tekbiyik-Ersoy, "Development of renewable energy in China, USA, and Brazil: A comparative study on renewable energy policies," Sustainability, vol. 12, no. 21, p. 9136, 2020, https://doi.org/10.3390/su12219136.
[33] H. Salem, "Predictive modelling for solar power-driven hybrid desalination system using artificial neural network regression with Adam optimization," Desalination, vol. 522, p. 115411, 2022, https://doi.org/10.1016/j.desal.2021.115411.
[34] S. Barhmi, O. Elfatni, and I. Belhaj, "Forecasting of wind speed using multiple linear regression and artificial neural networks," Energy Systems, vol. 11, no. 4, pp. 935-946, 2020, https://doi.org/10.1007/s12667-019-00338-y.
[35] D. Maulud and A. M. Abdulazeez, "A review on linear regression comprehensive in machine learning," Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140-147, 2020, https://doi.org/10.38094/jastt1457.
[36] S. A. Kalogirou, E. Mathioulakis, and V. Belessiotis, "Artificial neural networks for the performance prediction of large solar systems," Renewable Energy, vol. 63, pp. 90-97, 2014, https://doi.org/10.1016/j.renene.2013.08.049.
[37] M. Abuella and B. Chowdhury, "Solar power probabilistic forecasting by using multiple linear regression analysis," SoutheastCon 2015, 2015, https://doi.org/10.1109/SECON.2015.7132869.
[38] A. Balal and M. Herrera, "Design a Power Converter to Charge a Hybrid Electric Vehicle," 2021 IEEE 18th International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI (HONET), 2021, https://doi.org/10.1109/HONET53078.2021.9615492.
[39] A. Palomino and M. Parvania, "Data-driven risk analysis of joint electric vehicle and solar operation in distribution networks," IEEE Open Access Journal of Power and Energy, vol. 7, pp. 141-150, 2020, https://doi.org/10.1109/OAJPE.2020.2984696.
[40] F. He and H. Fathabadi, "Novel standalone plug-in hybrid electric vehicle charging station fed by solar energy in presence of a fuel cell system used as supporting power source," Renewable Energy, vol. 156, pp. 964-974, 2020, https://doi.org/10.1016/j.renene.2020.04.141.
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Pouya Pourmaleki, Nima Pourmaleki, Ali Rezaei
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia