AI-Driven Solar Energy Generation and Smart Grid Integration A Holistic Approach to Enhancing Renewable Energy Efficiency
Keywords:
Artificial Intelligence, Solar Energy, Smart Grids, Renewable Energy EfficiencyAbstract
This paper comprehensively analyzes AI-driven solar energy generation and smart grid integration, focusing on enhancing renewable energy efficiency. The study examines applying advanced artificial intelligence techniques in optimizing solar power production, forecasting, and grid management. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are evaluated for effectiveness in solar irradiance prediction and PV system performance estimation. The integration of AI in smart grids is explored, highlighting its role in demand-side management, energy storage optimization, and grid stability control. A holistic approach to improving renewable energy efficiency is proposed, encompassing integrated AI frameworks for solar-plus-storage systems, multi-objective optimization techniques for energy management, and AI-enabled microgrids and virtual power plants. The paper also addresses the challenges and future trends in AI application to renewable energy systems, including scalability issues, regulatory considerations, and ethical implications. By leveraging big data analytics and advanced AI algorithms, this research demonstrates the potential for significant improvements in overall system efficiency, reliability, and sustainability of solar energy systems integrated with smart grids.
References
A. Gupta, R. Saxena, S. Gupta, Kavita, and S. Kumar, "A Comprehensive Survey on Role of Artificial Intelligence in Solar Energy Processes," in 2022 IEEE 7th International Conference for Convergence in Technology (I2CT), 2022, pp. 1-6. https://doi.org/10.1109/I2CT54291.2022.9824314
T. V. Nguyen, "Applications of Artificial Intelligence in Renewable Energy: A Brief Review," in 2023 International Conference on System Science and Engineering (ICSSE), 2023, pp. 348-351. https://doi.org/10.1109/ICSSE58758.2023.10227160
J. T. Dellosa and E. C. Palconit, "Artificial Intelligence (AI) in Renewable Energy Systems: A Condensed Review of its Applications and Techniques," in 2021 IEEE International Conference on Environment and Electrical Engineering and 2021 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2021, pp. 1-6. https://doi.org/10.1109/EEEIC/ICPSEurope51590.2021.9584587
S. Pant, R. Singh, P. Rawat, Y. Chanti, S. Kathuria, and V. Pachouri, "Artificial Intelligence and Internet of Things Intersection in Green Energy," in 2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT), 2023, pp. 1-5. https://doi.org/10.1109/CISCT57197.2023.10351314
D. Bouabdallaoui, F. Elmariami, T. Haidi, A. Tarraq, and M. Derri, "Artificial Intelligence Methods Applied to Wind and Solar Energy Forecasting: A Comparative Study of Current Techniques," in 2023 International Conference on Digital Age & Technological Advances for Sustainable Development (ICDATA), 2023, pp. 94-99. https://doi.org/10.1109/ICDATA58816.2023.00026
S. Li, H. Xu, T. Lu, G. Cao, and X. Zhang, "Emerging Technologies in Finance: Revolutionizing Investment Strategies and Tax Management in the Digital Era," Management Journal for Advanced Research, vol. 4, no. 4, pp. 35-49, 2024. https://doi.org/10.5281/zenodo.13283670
J. Shi, F. Shang, S. Zhou, et al., "Applications of Quantum Machine Learning in Large-Scale E-commerce Recommendation Systems: Enhancing Efficiency and Accuracy," Journal of Industrial Engineering and Applied Science, vol. 2, no. 4, pp. 90-103, 2024. https://doi.org/10.5281/zenodo.13117899
S. Wang, H. Zheng, X. Wen, and S. Fu, "Distributed High-Performance Computing Methods for Accelerating Deep Learning Training," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 108-126, 2024. https://doi.org/10.60087/jklst.v3.n4.p22
M. Zhang, B. Yuan, H. Li, and K. Xu, "LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-Based Code Completion," Journal of Artificial Intelligence General Science (JAIGS), vol. 5, no. 1, pp. 295-326, 2024. https://doi.org/10.60087/jaigs.v5i1.200
H. Lei, B. Wang, Z. Shui, P. Yang, and P. Liang, "Automated Lane Change Behavior Prediction and Environmental Perception Based on SLAM Technology," https://doi.org/10.48550/arXiv.2404.04492
B. Wang, Y. He, Z. Shui, Q. Xin, and H. Lei, "Predictive Optimization of DDoS Attack Mitigation in Distributed Systems Using Machine Learning," Applied and Computational Engineering, vol. 64, pp. 95-100, 2024. Available from: https://doi.org/10.13140/RG.2.2.15938.39369
B. Wang, H. Zheng, K. Qian, X. Zhan, and J. Wang, "Edge Computing and AI-Driven Intelligent Traffic Monitoring and Optimization," Applied and Computational Engineering, vol. 77, pp. 225-230, 2024. https://doi.org/10.54254/2755-2721/77/2024MA0062
Y. Xu, Y. Liu, H. Xu, and H. Tan, "AI-Driven UX/UI Design: Empirical Research and Applications in FinTech," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 99-109, 2024. https://doi.org/10.55524/ijircst.2024.12.4.16
Y. Liu, Y. Xu, and R. Song, "Transforming User Experience (UX) Through Artificial Intelligence (AI) in Interactive Media Design," Engineering Science & Technology Journal, vol. 5, no. 7, pp. 2273-2283, 2024. https://doi.org/10.51594/estj.v5i7.1325
P. Zhang, "A Study on the Location Selection of Logistics Distribution Centers Based on E-Commerce," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. 103-107, 2024. https://doi.org/10.60087/jklst.vol3.n3.p103-107
P. Zhang and L. Gan, "Optimization of Vehicle Scheduling for Joint Distribution in the Logistics Park Based on Priority," Journal of Industrial Engineering and Applied Science, vol. 2, no. 4, pp. 116-121, 2024. https://doi.org/10.5281/zenodo.13120171
H. Xu, K. Niu, T. Lu, and S. Li, "Leveraging Artificial Intelligence for Enhanced Risk Management in Financial Services: Current Applications and Prospects," Engineering Science & Technology Journal, vol. 5, no. 8, pp. 2402-2426, 2024. https://doi.org/10.51594/estj.v5i8.1363
Y. Shi, F. Shang, Z. Xu, and S. Zhou, "Emotion-Driven Deep Learning Recommendation Systems: Mining Preferences from User Reviews and Predicting Scores," Journal of Artificial Intelligence and Development, vol. 3, no. 1, pp. 40-46, 2024. https://edujavare.com/index.php/JAI/article/view/472
S. Wang, K. Xu, and Z. Ling, "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 77-87, 2024. https://doi.org/10.55524/ijircst.2024.12.4.13
M. Zhang, B. Yuan, H. Li, and K. Xu, "LLM-Cloud Complete: Leveraging Cloud Computing for Efficient Large Language Model-Based Code Completion," Journal of Artificial Intelligence General Science (JAIGS), vol. 5, no. 1, pp. 295-326, 2024. https://doi.org/10.60087/jaigs.v5i1.200
B. Liu, X. Zhao, H. Hu, Q. Lin, and J. Huang, "Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN," Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, pp. 36-42, 2023. https://doi.org/10.53469/jtpes.2023.03(12).06
B. Liu, L. Yu, C. Che, Q. Lin, H. Hu, and X. Zhao, "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms," Applied and Computational Engineering, vol. 64, pp. 36-41, 2024. https://doi.org/10.48550/arXiv.2312.12872
B. Liu, "Based on Intelligent Advertising Recommendations and Abnormal Advertising Monitoring Systems in the Field of Machine Learning," International Journal of Computer Science and Information Technology, vol. 1, no. 1, pp. 17-23, 2023. https://doi.org/10.62051/ijcsit.v1n1.03
P. Liang, B. Song, X. Zhan, Z. Chen, and J. Yuan, "Automating the Training and Deployment of Models in MLOps by Integrating Systems with Machine Learning," Applied and Computational Engineering, vol. 67, pp. 1-7, 2024. https://doi.org/10.48550/arXiv.2405.09819
B. Wu, Y. Gong, H. Zheng, Y. Zhang, J. Huang, and J. Xu, "Enterprise Cloud Resource Optimization and Management Based on Cloud Operations," Applied and Computational Engineering, vol. 67, pp. 8-14, 2024. https://doi.org/10.54254/2755-2721/76/20240667
K. Xu, H. Zhou, H. Zheng, M. Zhu, and Q. Xin, "Intelligent Classification and Personalized Recommendation of E-Commerce Products Based on Machine Learning," arXiv preprint arXiv:2403.19345, 2024. https://doi.org/10.48550/arXiv.2403.19345
H. Zheng, K. Xu, H. Zhou, Y. Wang, and G. Su, "Medication Recommendation System Based on Natural Language Processing for Patient Emotion Analysis," Academic Journal of Science and Technology, vol. 10, no. 1, pp. 62-68, 2024. https://doi.org/10.54097/v160aa61
S. Wang, K. Xu, and Z. Ling, "Deep Learning-Based Chip Power Prediction and Optimization: An Intelligent EDA Approach," International Journal of Innovative Research in Computer Science & Technology, vol. 12, no. 4, pp. 77-87, 2024. https://doi.org/10.55524/ijircst.2024.12.4.13
L. Guo, Z. Li, K. Qian, W. Ding, and Z. Chen, "Bank Credit Risk Early Warning Model Based on Machine Learning Decision Trees," Journal of Economic Theory and Business Management, vol. 1, no. 3, pp. 24-30, 2024. https://doi.org/10.5281/zenodo.11627011
Z. Xu, L. Guo, S. Zhou, R. Song, and K. Niu, "Enterprise Supply Chain Risk Management and Decision Support Driven by Large Language Models," Applied Science and Engineering Journal for Advanced Research, vol. 3, no. 4, pp. 1-7, 2024. https://doi.org/10.5281/zenodo.12670581
R. Song, Z. Wang, L. Guo, F. Zhao, and Z. Xu, "Deep Belief Networks (DBN) for Financial Time Series Analysis and Market Trends Prediction," World Journal of Innovative Medical Technologies, vol. 5, no. 3, pp. 27-34, 2024. https://doi.org/10.53469/wjimt.2024.07(04).01
H. Zheng, J. Wu, R. Song, L. Guo, and Z. Xu, "Predicting Financial Enterprise Stocks and Economic Data Trends Using Machine Learning Time Series Analysis," Applied and Computational Engineering, vol. 87, pp. 26-32, 2024. https://doi.org/10.20944/preprints202407.0895.v1
T. Yang, Q. Xin, X. Zhan, S. Zhuang, and H. Li, "Enhancing Financial Services through Big Data and AI-Driven Customer Insights and Risk Analysis," Journal of Knowledge Learning and Science Technology, vol. 3, no. 3, pp. https://doi.org/10.60087/jklst.vol3.n3.p53-62
X. Zhan, Z. Ling, Z. Xu, L. Guo, and 53-62, 2024. S. Zhuang, "Driving Efficiency and Risk Management in Finance through AI and RPA," Unique Endeavor in Business & Social Sciences, vol. 3, no. 1, pp. 189-197, 2024. https://unbss.com/index.php/unbss/article/view/50/49
Y. Feng, Y. Qi, H. Li, X. Wang, and J. Tian, "Leveraging Federated Learning and Edge Computing for Recommendation Systems within Cloud Computing Networks," in Proceedings of the Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), vol. 13210, pp. 279-287, 2024. https://doi.org/10.1117/12.3034773
P. Yang, Z. Chen, G. Su, H. Lei, and B. Wang, "Enhancing Traffic Flow Monitoring with Machine Learning Integration on Cloud Data Warehousing," Applied and Computational Engineering, vol. 67, pp. 15-21, 2024. https://doi.org/10.54254/2755-2721/77/2024MA0058
W. Jiang, K. Qian, C. Fan, W. Ding, and Z. Li, "Applications of Generative AI-Based Financial Robot Advisors as Investment Consultants," Applied and Computational Engineering, vol. 67, pp. 28-33, 2024. https://doi.org/10.54254/2755-2721/77/2024MA0057
C. Fan, Z. Li, W. Ding, H. Zhou, and K. Qian, "Integrating Artificial Intelligence with SLAM Technology for Robotic Navigation and Localization in Unknown Environments," International Journal of Robotics and Automation, vol. 29, no. 4, pp. 215-230, 2024. Available from: https://doi.org/10.13140/RG.2.2.13091.67360
C. Fan, W. Ding, K. Qian, H. Tan, and Z. Li, "Cueing Flight Object Trajectory and Safety Prediction Based on SLAM Technology," Journal of Theory and Practice of Engineering Science, vol. 4, no. 5, pp. 1-8, 2024. https://doi.org/10.53469/jtpes.2024.04(05).01
W. Jiang, T. Yang, A. Li, Y. Lin, and X. Bai, "The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis," Academic Journal of Sociology and Management, vol. 2, no. 3, pp. 40-46, 2024. https://doi.org/10.5281/zenodo.11112424
K. Xu, H. Zheng, X. Zhan, S. Zhou, and K. Niu, "Evaluation and Optimization of Intelligent Recommendation System Performance with Cloud Resource Automation Compatibility," Applied and Computational Engineering, vol. 87, pp. 33-40, 2024. https://doi.org/10.20944/preprints202407.2199.v1
F. Zhao, H. Li, K. Niu, J. Shi, and R. Song, "Application of Deep Learning-Based Intrusion Detection System (IDS) in Network Anomaly Traffic Detection," Journal of Network Security and Systems Management, vol. 2, no. 1, pp. 47-53, 2024. https://doi.org/10.20944/preprints202407.0595.v1
