Abstract
Industrialization, urbanization, population expansion, and changes in lifestyles within the Group of Seven (G7) have raised the danger of global warming since CO2 emissions directly impact the quantity of power that can be produced from diverse sources. However, the intrinsic energy needs and CO2 emissions found in renewable energy, especially solar cells and associated equipment, which have been extensively embraced in low-income nations, are seldom, if ever, considered by decision-makers. We propose converting a conventional neural network into a quantum photonic system. First, the classical neurons are made reversible by adding extra bits. After that, unitarity and quantum reversibility are added to the list. This work provides a unique approach to lowering carbon emissions based on environmentally friendly renewable solar cells and environmental thermal image analysis using machine learning architectures. The ambient thermal picture collected from both developed and developing countries was processed using convolutional adversarial Gaussian markov neural networks. The usage of eco-renewable solar cells has led to a reduction in carbon emissions in both industrialized and developing countries. The results of the experiments are broken down into many categories, including prediction accuracy, energy consumption, resilience, execution time, and mean average precision.
Similar content being viewed by others
Data availability
All the data’s available in the manuscript.
References
Abdullah-Al-Mahbub, M., Islam, A.R.M.T., Almohamad, H., Al Dughairi, A.A., Al-Mutiry, M., Abdo, H.G.: Different forms of solar energy progress: the fast-growing eco-friendly energy source in Bangladesh for a sustainable future. Energies 15(18), 6790 (2022)
Aboud, L.M., Farid, O.M.: Eco-friendly Suez Canal ferries incorporating PV/shore connection hybrid power system. Nav. Eng. J. 134(4), 127–136 (2022)
Alqaed, S., Mustafa, J., Almehmadi, F.A., Alharthi, M.A., Sharifpur, M., Cheraghian, G.: Machine learning-based approach for modeling the nanofluid flow in a solar thermal panel in the presence of phase change materials. Processes 10(11), 2291 (2022)
Aly, A.M., Clarke, J.: Wind design of solar panels for resilient and green communities: CFD with machine learning. Sustain. Cities Soc. 94, 104529 (2023)
Haider, S.A., Sajid, M., Sajid, H., Uddin, E., Ayaz, Y.: Deep learning and statistical methods for short-and long-term solar irradiance forecasting for Islamabad. Renew. Energy 198, 51–60 (2022)
Kalaiselvi, B., Karthik, B., & Kumaravel, A. (2022, July). Variant Mode Data Analytics in Predicting the Radiation Effect on Solar Power Generation using Machine Learning Algorithms. In 2022 IEEE International Conference on Data Science and Information System (ICDSIS) (pp. 1–6). IEEE.
Khani, N., Manesh, M.H.K., Onishi, V.C.: Optimal 6E design of an integrated solar energy-driven polygeneration and CO2 capture system: a machine learning approach. Thermal Sci. Eng. Progress 38, 101669 (2023)
Kiehbadroudinezhad, M., Merabet, A., Rajabipour, A., Cada, M., Kiehbadroudinezhad, S., Khanali, M., Hosseinzadeh-Bandbafha, H.: Optimization of wind/solar energy microgrid by division algorithm considering human health and environmental impacts for power-water cogeneration. Energy Convers. Manage. 252, 115064 (2022)
Muthusamy, P.D., Velusamy, G., Thandavan, S., Govindasamy, B.R., Savarimuthu, N.: Industrial Internet of things-based solar photo voltaic cell waste management in next generation industries. Environ. Sci. Pollut. Res. 29(24), 35542–35556 (2022)
Raihan, A.: Toward sustainable and green development in Chile: dynamic influences of carbon emission reduction variables. Innov. Green Dev. 2(2), 100038 (2023a)
Raihan, A.: The dynamic nexus between economic growth, renewable energy use, urbanization, industrialization, tourism, agricultural productivity, forest area, and carbon dioxide emissions in the Philippines. Energy Nexus 9, 100180 (2023b)
Raihan, A., Muhtasim, D.A., Farhana, S., Rahman, M., Hasan, M.A.U., Paul, A., Faruk, O.: Dynamic linkages between environmental factors and carbon emissions in Thailand. Environ. Process. 10(1), 5 (2023)
Rasool, S.F., Zaman, S., Jehan, N., Chin, T., Khan, S., uz Zaman, Q.: Investigating the role of the tech industry, renewable energy, and urbanization in sustainable environment: policy directions in the context of developing economies. Technol. Forecast. Soc. Chang. 183, 121935 (2022)
Stergiou, K., Ntakolia, C., Varytis, P., Koumoulos, E., Karlsson, P., Moustakidis, S.: Enhancing property prediction and process optimization in building materials through machine learning: A review. Comput. Mater. Sci. 220, 112031 (2023)
Sujith, A.V.L.N., Swathi, R., Venkatasubramanian, R., Venu, N., Hemalatha, S., George, T., Osman, S.M.: Integrating nanomaterial and high-performance fuzzy-based machine learning approach for green energy conversion. J. Nanomater. 2022, 1–11 (2022)
Vallikannu, R., Kishankumar, K., Kumar, B. V., Raj, D. G., & Reddy, Y. J. C. (2023, January). Novel method of implementation of solar based smart cycle and solar power consumption prediction using machine learning. In AIP Conference Proceedings (Vol. 2523, No. 1, p. 020028). AIP Publishing LLC.
You, C., Khattak, S.I., Ahmad, M.: Impact of innovation in solar photovoltaic energy generation distribution or transmission-related technologies on carbon dioxide emissions in China. J. Knowl. Econ. (2023). https://doi.org/10.1007/s13132-023-01284-y
Funding
This research not received any fund.
Author information
Authors and Affiliations
Contributions
FS Conceived and design the analysis Writing—Original draft preparation. Collecting the Data, Contributed data and analysis stools, YW Performed and analysis, LW Performed and analysis, Wrote the Paper Editing and Figure Design.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical approval
This article does not contain any studies with animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Su, F., Wang, Y. & Wang, L. Comparative analysis optical communication based renewable solar cell and quantum network for the reduction of carbon emission. Opt Quant Electron 55, 860 (2023). https://doi.org/10.1007/s11082-023-05140-w
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11082-023-05140-w