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      Leveraging Machine Learning for Prediction and Optimizing Renewable Energy Systems

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      ScienceOpen Preprints
      Renewable energy, Machine Learning, Energy forecasting, Optimization, Sustainable


            Renewable energy systems play a critical role in the transition to a more sustainable future. However, these systems are often characterized by significant fluctuations in energy output due to changes in weather and other environmental factors. In recent years, machine learning algorithms have emerged as a powerful tool for predicting and optimizing renewable energy systems. This paper provides an overview of the latest research in this area, including techniques for predicting solar radiation and wind power output, as well as algorithms for optimizing energy storage and grid stability. The paper also explores the potential of machine learning to revolutionize the way we generate, distribute, and consume energy, paving the way for a cleaner, more sustainable future. By leveraging the power of artificial intelligence, we can unlock the full potential of renewable energy systems and create a more resilient, secure, and efficient energy infrastructure.


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            ScienceOpen Preprints
            14 March 2023
            [1 ] Department of Energy Engineering and Industry, Science and Research Branch, Islamic Azad University, Tehran, Iran;
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            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            : 14 March 2023

            All data generated or analysed during this study are included in this published article (and its supplementary information files).
            Earth & Environmental sciences,Computer science,Engineering
            Renewable energy, Machine Learning, Energy forecasting, Optimization, Sustainable


            1. Xu Jiuping, Li Qiulin, Xie Heping, Ni Ting, Ouyang Chi. Tech-integrated paradigm based approaches towards carbon-free hydrogen production. Renewable and Sustainable Energy Reviews. Vol. 82:4279–4295. 2018. Elsevier BV. [Cross Ref]

            2. Chatterjee Shantanu, Kumar Prashant, Chatterjee Saibal. A techno-commercial review on grid connected photovoltaic system. Renewable and Sustainable Energy Reviews. Vol. 81:2371–2397. 2018. Elsevier BV. [Cross Ref]

            3. Oliva H. Sebastian, Passey Rob, Abdullah Md Abu. A semi-empirical financial assessment of combining residential photovoltaics, energy efficiency and battery storage systems. Renewable and Sustainable Energy Reviews. Vol. 105:206–214. 2019. Elsevier BV. [Cross Ref]

            4. Ameen Mariam, Azizan Mohammad Tazli, Yusup Suzana, Ramli Anita, Yasir Madiha. Catalytic hydrodeoxygenation of triglycerides: An approach to clean diesel fuel production. Renewable and Sustainable Energy Reviews. Vol. 80:1072–1088. 2017. Elsevier BV. [Cross Ref]

            5. Judge Frances, McAuliffe Fiona Devoy, Sperstad Iver Bakken, Chester Rachel, Flannery Brian, Lynch Katie, Murphy Jimmy. A lifecycle financial analysis model for offshore wind farms. Renewable and Sustainable Energy Reviews. Vol. 103:370–383. 2019. Elsevier BV. [Cross Ref]

            6. Kim Albert S., Kim Hyeon-Ju, Lee Ho-Saeng, Cha Sangwon. Dual-use open cycle ocean thermal energy conversion (OC-OTEC) using multiple condensers for adjustable power generation and seawater desalination. Renewable Energy. Vol. 85:344–358. 2016. Elsevier BV. [Cross Ref]

            7. Aghahosseini Arman, Bogdanov Dmitrii, Barbosa Larissa S.N.S., Breyer Christian. Analysing the feasibility of powering the Americas with renewable energy and inter-regional grid interconnections by 2030. Renewable and Sustainable Energy Reviews. Vol. 105:187–205. 2019. Elsevier BV. [Cross Ref]

            8. Yu Wenbin, Zhao Feiyang, Yang Wenming. Qualitative analysis of particulate matter emission from diesel engine fueled with Jet A-1 under multivariate combustion boundaries by principal component analysis. Applied Energy. Vol. 269:2020. Elsevier BV. [Cross Ref]

            9. Salam Satishchandra, Verma Tikendra Nath. Appending empirical modelling to numerical solution for behaviour characterisation of microalgae biodiesel. Energy Conversion and Management. Vol. 180:496–510. 2019. Elsevier BV. [Cross Ref]

            10. Emadi Mohammad Ali, Mahmoudimehr Javad. Modeling and thermo-economic optimization of a new multi-generation system with geothermal heat source and LNG heat sink. Energy Conversion and Management. Vol. 189:153–166. 2019. Elsevier BV. [Cross Ref]

            11. Kim Tea-Woo, Woo Nam-Sub, Han Sang-Mok, Kim Young-Ju. Optimization and Extended Applicability of Simplified Slug Flow Model for Liquid-Gas Flow in Horizontal and Near Horizontal Pipes. Energies. Vol. 13(4)2020. MDPI AG. [Cross Ref]

            12. Ju Dehao, Huang Zhong, Li Xiang, Zhang Tingting, Cai Weiwei. Comparison of open chamber and pre-chamber ignition of methane/air mixtures in a large bore constant volume chamber: Effect of excess air ratio and pre-mixed pressure. Applied Energy. Vol. 260:2020. Elsevier BV. [Cross Ref]

            13. Zhang Yu, Zhang Yanjun, Yu Hai, Li Jianming, Xie Yangyang, Lei Zhihong. Geothermal resource potential assessment of Fujian Province, China, based on geographic information system (GIS) -supported models. Renewable Energy. Vol. 153:564–579. 2020. Elsevier BV. [Cross Ref]

            14. Gandomi Amir Hossein, Alavi Amir Hossein. Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation. Vol. 17(12):4831–4845. 2012. Elsevier BV. [Cross Ref]

            15. Liu Junwei, Zhang Ji, Zhang Debao, Jiao Shifei, Xing Jincheng, Tang Huajie, Zhang Ying, Li Shuai, Zhou Zhihua, Zuo Jian. Sub-ambient radiative cooling with wind cover. Renewable and Sustainable Energy Reviews. Vol. 130:2020. Elsevier BV. [Cross Ref]

            16. Kim Jung Eun, Tang Tian. Preventing early lock-in with technology-specific policy designs: The Renewable Portfolio Standards and diversity in renewable energy technologies. Renewable and Sustainable Energy Reviews. Vol. 123:2020. Elsevier BV. [Cross Ref]

            17. Swiergiel Weronika, Manduric Sanja, Rämert Birgitta, Porcel Mario, Tasin Marco. Development of sustainable plant protection programs through multi-actor Co-innovation: An 8-year case study in Swedish apple production. Journal of Cleaner Production. Vol. 234:1178–1191. 2019. Elsevier BV. [Cross Ref]

            18. Udeh Godfrey T., Michailos Stavros, Ingham Derek, Hughes Kevin J., Ma Lin, Pourkashanian Mohamed. A techno-enviro-economic assessment of a biomass fuelled micro-CCHP driven by a hybrid Stirling and ORC engine. Energy Conversion and Management. Vol. 227:2021. Elsevier BV. [Cross Ref]


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