AI-Driven Robotics in Solar and Wind Energy Maintenance: A Path toward Sustainability
DOI:
https://doi.org/10.18034/apjee.v9i2.784Keywords:
AI-driven Robotics, Renewable Energy, Solar Energy, Wind Energy, Predictive Maintenance, Autonomous Systems, Sustainability, Robotics ApplicationsAbstract
According to this research, AI-driven robots may improve operational efficiency, save costs, and promote sustainability objectives in solar and wind energy system maintenance. The paper examines AI and robotics technology, analyzes their applications in renewable energy maintenance, and identifies difficulties and prospects for maximizing their utilization. Using secondary data, the research synthesizes significant findings and trends from peer-reviewed publications, case studies, and industry reports. Primary results show that AI-driven robots may transform maintenance processes by boosting inspection accuracy, safety, and downtime while meeting sustainability objectives via resource efficiency and waste reduction. High initial costs, technological constraints in severe settings, and regulatory complexity still prevent broad implementation. Policy implications involve focused research and development, consistent rules, and financial incentives to make these technologies more accessible to smaller operators to solve these difficulties. Governments, business leaders, and academics must work together to overcome these challenges and maximize AI-driven robots in renewable energy. This research stresses robots' crucial role in expediting sustainable energy infrastructure transformation.
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Ahmmed, S., Narsina, D., Addimulam, S., & Boinapalli, N. R. (2021). AI-Powered Financial Engineering: Optimizing Risk Management and Investment Strategies. Asian Accounting and Auditing Advancement, 12(1), 37–45. https://4ajournal.com/article/view/96
Benavente-Peces, C. (2019). On the Energy Efficiency in the Next Generation of Smart Buildings—Supporting Technologies and Techniques. Energies, 12(22). https://doi.org/10.3390/en12224399
Bokde, N., Feijóo, A., Villanueva, D., Kulat, K. (2019). A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction. Energies, 12(2), 254. https://doi.org/10.3390/en12020254
Chakraborty, T., Majumder, M. (2019). Application of Statistical Charts, Multi-criteria Decision Making and Polynomial Neural Networks in Monitoring Energy Utilization of Wave Energy Converters. Environment, Development and Sustainability, 21(1), 199-219. https://doi.org/10.1007/s10668-017-0030-x
Deming, C., Pasam, P., Allam, A. R., Mohammed, R., Venkata, S. G. N., & Kothapalli, K. R. V. (2021). Real-Time Scheduling for Energy Optimization: Smart Grid Integration with Renewable Energy. Asia Pacific Journal of Energy and Environment, 8(2), 77-88. https://doi.org/10.18034/apjee.v8i2.762
Devarapu, K. (2020). Blockchain-Driven AI Solutions for Medical Imaging and Diagnosis in Healthcare. Technology & Management Review, 5, 80-91. https://upright.pub/index.php/tmr/article/view/165
Devarapu, K. (2021). Advancing Deep Neural Networks: Optimization Techniques for Large-Scale Data Processing. NEXG AI Review of America, 2(1), 47-61.
Devarapu, K., Rahman, K., Kamisetty, A., & Narsina, D. (2019). MLOps-Driven Solutions for Real-Time Monitoring of Obesity and Its Impact on Heart Disease Risk: Enhancing Predictive Accuracy in Healthcare. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 43-55. https://upright.pub/index.php/ijrstp/article/view/160
Fu-Cheng, W., Kuang-Ming, L. (2019). Impacts of Load Profiles on the Optimization of Power Management of a Green Building Employing Fuel Cells. Energies, 12(1), 57. https://doi.org/10.3390/en12010057
Gabbar, H. A., Zidan, A. (2016). Modeling, Evaluation, and Optimization of Gas-power and Energy Supply Scenarios. Frontiers in Energy, 10(4), 393-408. https://doi.org/10.1007/s11708-016-0422-x
Gade, P. K. (2019). MLOps Pipelines for GenAI in Renewable Energy: Enhancing Environmental Efficiency and Innovation. Asia Pacific Journal of Energy and Environment, 6(2), 113-122. https://doi.org/10.18034/apjee.v6i2.776
Gade, P. K., Sridharlakshmi, N. R. B., Allam, A. R., & Koehler, S. (2021). Machine Learning-Enhanced Beamforming with Smart Antennas in Wireless Networks. ABC Journal of Advanced Research, 10(2), 207-220. https://doi.org/10.18034/abcjar.v10i2.770
Goda, D. R. (2020). Decentralized Financial Portfolio Management System Using Blockchain Technology. Asian Accounting and Auditing Advancement, 11(1), 87–100. https://4ajournal.com/article/view/87
Gong, Y., Yang, Z., Shan, X., Sun, Y., Xie, T. (2019). Capturing Flow Energy from Ocean and Wind. Energies, 12(11), 2184. https://doi.org/10.3390/en12112184
Gummadi, J. C. S., Narsina, D., Karanam, R. K., Kamisetty, A., Talla, R. R., & Rodriguez, M. (2020). Corporate Governance in the Age of Artificial Intelligence: Balancing Innovation with Ethical Responsibility. Technology & Management Review, 5, 66-79. https://upright.pub/index.php/tmr/article/view/157
Gummadi, J. C. S., Thompson, C. R., Boinapalli, N. R., Talla, R. R., & Narsina, D. (2021). Robotics and Algorithmic Trading: A New Era in Stock Market Trend Analysis. Global Disclosure of Economics and Business, 10(2), 129-140. https://doi.org/10.18034/gdeb.v10i2.769
Gupta, M. C., Carlson, D. E. (2015). Laser Processing of Materials for Renewable Energy Applications. MRS Energy & Sustainability, 2(1), 2. https://doi.org/10.1557/mre.2015.3
Kamisetty, A., Onteddu, A. R., Kundavaram, R. R., Gummadi, J. C. S., Kothapalli, S., Nizamuddin, M. (2021). Deep Learning for Fraud Detection in Bitcoin Transactions: An Artificial Intelligence-Based Strategy. NEXG AI Review of America, 2(1), 32-46.
Karanam, R. K., Natakam, V. M., Boinapalli, N. R., Sridharlakshmi, N. R. B., Allam, A. R., Gade, P. K., Venkata, S. G. N., Kommineni, H. P., & Manikyala, A. (2018). Neural Networks in Algorithmic Trading for Financial Markets. Asian Accounting and Auditing Advancement, 9(1), 115–126. https://4ajournal.com/article/view/95
Kommineni, H. P. (2019). Cognitive Edge Computing: Machine Learning Strategies for IoT Data Management. Asian Journal of Applied Science and Engineering, 8(1), 97-108. https://doi.org/10.18034/ajase.v8i1.123
Kommineni, H. P. (2020). Automating SAP GTS Compliance through AI-Powered Reciprocal Symmetry Models. International Journal of Reciprocal Symmetry and Theoretical Physics, 7, 44-56. https://upright.pub/index.php/ijrstp/article/view/162
Kommineni, H. P., Fadziso, T., Gade, P. K., Venkata, S. S. M. G. N., & Manikyala, A. (2020). Quantifying Cybersecurity Investment Returns Using Risk Management Indicators. Asian Accounting and Auditing Advancement, 11(1), 117–128. Retrieved from https://4ajournal.com/article/view/97
Kothapalli, S. (2021). Blockchain Solutions for Data Privacy in HRM: Addressing Security Challenges. Journal of Fareast International University, 4(1), 17-25. https://jfiu.weebly.com/uploads/1/4/9/0/149099275/2021_3.pdf
Kothapalli, S., Manikyala, A., Kommineni, H. P., Venkata, S. G. N., Gade, P. K., Allam, A. R., Sridharlakshmi, N. R. B., Boinapalli, N. R., Onteddu, A. R., & Kundavaram, R. R. (2019). Code Refactoring Strategies for DevOps: Improving Software Maintainability and Scalability. ABC Research Alert, 7(3), 193–204. https://doi.org/10.18034/ra.v7i3.663
Kundavaram, R. R., Rahman, K., Devarapu, K., Narsina, D., Kamisetty, A., Gummadi, J. C. S., Talla, R. R., Onteddu, A. R., & Kothapalli, S. (2018). Predictive Analytics and Generative AI for Optimizing Cervical and Breast Cancer Outcomes: A Data-Centric Approach. ABC Research Alert, 6(3), 214-223. https://doi.org/10.18034/ra.v6i3.672
Manikyala, A. (2022). Sentiment Analysis in IoT Data Streams: An NLP-Based Strategy for Understanding Customer Responses. Silicon Valley Tech Review, 1(1), 35-47.
Najjar, M. K., Tam, V. W. Y., Di Gregorio, L. T., Evangelista, A. C. J., Hammad, A. W. A. (2019). Integrating Parametric Analysis with Building Information Modeling to Improve Energy Performance of Construction Projects. Energies, 12(8), 1515. https://doi.org/10.3390/en12081515
Narsina, D., Devarapu, K., Kamisetty, A., Gummadi, J. C. S., Richardson, N., & Manikyala, A. (2021). Emerging Challenges in Mechanical Systems: Leveraging Data Visualization for Predictive Maintenance. Asian Journal of Applied Science and Engineering, 10(1), 77-86. https://doi.org/10.18034/ajase.v10i1.124
Narsina, D., Gummadi, J. C. S., Venkata, S. S. M. G. N., Manikyala, A., Kothapalli, S., Devarapu, K., Rodriguez, M., & Talla, R. R. (2019). AI-Driven Database Systems in FinTech: Enhancing Fraud Detection and Transaction Efficiency. Asian Accounting and Auditing Advancement, 10(1), 81–92. https://4ajournal.com/article/view/98
Onteddu, A. R., Rahman, K., Roberts, C., Kundavaram, R. R., Kothapalli, S. (2022). Blockchain-Enhanced Machine Learning for Predictive Analytics in Precision Medicine. Silicon Valley Tech Review, 1(1), 48-60. https://www.siliconvalley.onl/uploads/9/9/8/2/9982776/2022.4
Onteddu, A. R., Venkata, S. S. M. G. N., Ying, D., & Kundavaram, R. R. (2020). Integrating Blockchain Technology in FinTech Database Systems: A Security and Performance Analysis. Asian Accounting and Auditing Advancement, 11(1), 129–142. https://4ajournal.com/article/view/99
Richardson, N., Manikyala, A., Gade, P. K., Venkata, S. S. M. G. N., Asadullah, A. B. M., & Kommineni, H. P. (2021). Emergency Response Planning: Leveraging Machine Learning for Real-Time Decision-Making. Technology & Management Review, 6, 50-62. https://upright.pub/index.php/tmr/article/view/163
Roberts, C., Kundavaram, R. R., Onteddu, A. R., Kothapalli, S., Tuli, F. A., Miah, M. S. (2020). Chatbots and Virtual Assistants in HRM: Exploring Their Role in Employee Engagement and Support. NEXG AI Review of America, 1(1), 16-31.
Rodriguez, M., Sridharlakshmi, N. R. B., Boinapalli, N. R., Allam, A. R., & Devarapu, K. (2020). Applying Convolutional Neural Networks for IoT Image Recognition. International Journal of Reciprocal Symmetry and Theoretical Physics, 7, 32-43. https://upright.pub/index.php/ijrstp/article/view/158
Shadman, M., Silva, C., Faller, D., Wu, Z., de Freitas, L. P. A. (2019). Ocean Renewable Energy Potential, Technology, and Deployments: A Case Study of Brazil. Energies, 12(19), 3658. https://doi.org/10.3390/en12193658
Sridharlakshmi, N. R. B. (2020). The Impact of Machine Learning on Multilingual Communication and Translation Automation. NEXG AI Review of America, 1(1), 85-100.
Sridharlakshmi, N. R. B. (2021). Data Analytics for Energy-Efficient Code Refactoring in Large-Scale Distributed Systems. Asia Pacific Journal of Energy and Environment, 8(2), 89-98. https://doi.org/10.18034/apjee.v8i2.771
Sun, W., Lu, G., Cheng, Y., Chen, S., Hou, Y. (2018). The State of the Art: Application of Green Technology in Sustainable Pavement. Advances in Materials Science and Engineering, 2018. https://doi.org/10.1155/2018/9760464
Talla, R. R., Manikyala, A., Gade, P. K., Kommineni, H. P., & Deming, C. (2022). Leveraging AI in SAP GTS for Enhanced Trade Compliance and Reciprocal Symmetry Analysis. International Journal of Reciprocal Symmetry and Theoretical Physics, 9, 10-23. https://upright.pub/index.php/ijrstp/article/view/164
Talla, R. R., Manikyala, A., Nizamuddin, M., Kommineni, H. P., Kothapalli, S., Kamisetty, A. (2021). Intelligent Threat Identification System: Implementing Multi-Layer Security Networks in Cloud Environments. NEXG AI Review of America, 2(1), 17-31.
Thompson, C. R., Talla, R. R., Gummadi, J. C. S., Kamisetty, A (2019). Reinforcement Learning Techniques for Autonomous Robotics. Asian Journal of Applied Science and Engineering, 8(1), 85-96. https://ajase.net/article/view/94
Venkata, S. S. M. G. N., Gade, P. K., Kommineni, H. P., Manikyala, A., & Boinapalli , N. R. (2022). Bridging UX and Robotics: Designing Intuitive Robotic Interfaces. Digitalization & Sustainability Review, 2(1), 43-56. https://upright.pub/index.php/dsr/article/view/159
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