Study on the accuracy and stability of distributed photovoltaic customer load forecasting based on hybrid modeling

[1] Faheem, M., Shah, S. B. H., Butt, R. A., Raza, B., Anwar, M., Ashraf, M. W., … & Gungor, V. C. (2018). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges. Computer Science Review, 30, 1-30. Faheem M. Shah S. B. H. Butt R. A. Raza B. Anwar M. Ashraf M. W. Gungor V. C. ( 2018 ). Smart grid communication and information technologies in the perspective of Industry 4.0: Opportunities and challenges . Computer Science Review , 30 , 1 - 30 . Search in Google Scholar

[2] Daki, H., El Hannani, A., Aqqal, A., Haidine, A., & Dahbi, A. (2017). Big Data management in smart grid: concepts, requirements and implementation. Journal of Big Data, 4, 1-19. Daki H. El Hannani A. Aqqal A. Haidine A. Dahbi A. ( 2017 ). Big Data management in smart grid: concepts, requirements and implementation . Journal of Big Data , 4 , 1 - 19 . Search in Google Scholar

[3] Dileep, G. J. R. E. (2020). A survey on smart grid technologies and applications. Renewable energy, 146, 2589-2625. Dileep G. J. R. E. ( 2020 ). A survey on smart grid technologies and applications . Renewable energy , 146 , 2589 - 2625 . Search in Google Scholar

[4] Yoldaş, Y., Önen, A., Muyeen, S. M., Vasilakos, A. V., & Alan, I. (2017). Enhancing smart grid with microgrids: Challenges and opportunities. Renewable and Sustainable Energy Reviews, 72, 205-214. Yoldaş Y. Önen A. Muyeen S. M. Vasilakos A. V. Alan I. ( 2017 ). Enhancing smart grid with microgrids: Challenges and opportunities . Renewable and Sustainable Energy Reviews , 72 , 205 - 214 . Search in Google Scholar

[5] Omitaomu, O. A., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548-568. Omitaomu O. A. Niu H. ( 2021 ). Artificial intelligence techniques in smart grid: A survey . Smart Cities , 4 ( 2 ), 548 - 568 . Search in Google Scholar

[6] El Mrabet, Z., Kaabouch, N., El Ghazi, H., & El Ghazi, H. (2018). Cyber-security in smart grid: Survey and challenges. Computers & Electrical Engineering, 67, 469-482. El Mrabet Z. Kaabouch N. El Ghazi H. El Ghazi H. ( 2018 ). Cyber-security in smart grid: Survey and challenges . Computers … Electrical Engineering , 67 , 469 - 482 . Search in Google Scholar

[7] Pan, E., Liu, S., Liu, J., Qi, Q., & Guo, Z. (2020). The state grid corporation of China’s practice and outlook for promoting new energy development. Energy Conversion and Economics, 1(2), 71-80. Pan E. Liu S. Liu J. Qi Q. Guo Z. ( 2020 ). The state grid corporation of China’s practice and outlook for promoting new energy development . Energy Conversion and Economics , 1 ( 2 ), 71 - 80 . Search in Google Scholar

[8] Taha, M. Q. (2020). Advantages and recent advances of smart energy grid. Bulletin of Electrical Engineering and Informatics, 9(5), 1739-1746. Taha M. Q. ( 2020 ). Advantages and recent advances of smart energy grid . Bulletin of Electrical Engineering and Informatics , 9 ( 5 ), 1739 - 1746 . Search in Google Scholar

[9] Ge, Q., Guo, C., Jiang, H., Lu, Z., Yao, G., Zhang, J., & Hua, Q. (2020). Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM. IEEE transactions on cybernetics, 52(2), 1112-1124. Ge Q. Guo C. Jiang H. Lu Z. Yao G. Zhang J. Hua Q. ( 2020 ). Industrial power load forecasting method based on reinforcement learning and PSO-LSSVM . IEEE transactions on cybernetics , 52 ( 2 ), 1112 - 1124 . Search in Google Scholar

[10] Hong, Y., Zhou, Y., Li, Q., Xu, W., & Zheng, X. (2020). A deep learning method for short-term residential load forecasting in smart grid. IEEE Access, 8, 55785-55797. Hong Y. Zhou Y. Li Q. Xu W. Zheng X. ( 2020 ). A deep learning method for short-term residential load forecasting in smart grid . IEEE Access , 8 , 55785 - 55797 . Search in Google Scholar

[11] Ahmad, N., Ghadi, Y., Adnan, M., & Ali, M. (2022). Load forecasting techniques for power system: Research challenges and survey. IEEE Access, 10, 71054-71090. Ahmad N. Ghadi Y. Adnan M. Ali M. ( 2022 ). Load forecasting techniques for power system: Research challenges and survey . IEEE Access , 10 , 71054 - 71090 . Search in Google Scholar

[12] Kong, W., Dong, Z. Y., Jia, Y., Hill, D. J., Xu, Y., & Zhang, Y. (2017). Short-term residential load forecasting based on LSTM recurrent neural network. IEEE transactions on smart grid, 10(1), 841-851. Kong W. Dong Z. Y. Jia Y. Hill D. J. Xu Y. Zhang Y. ( 2017 ). Short-term residential load forecasting based on LSTM recurrent neural network . IEEE transactions on smart grid , 10 ( 1 ), 841 - 851 . Search in Google Scholar

[13] Taïk, A., & Cherkaoui, S. (2020, June). Electrical load forecasting using edge computing and federated learning. In ICC 2020-2020 IEEE international conference on communications (ICC) (pp. 1-6). IEEE. Taïk A. Cherkaoui S. ( 2020 , June ). Electrical load forecasting using edge computing and federated learning . In ICC 2020-2020 IEEE international conference on communications (ICC) (pp. 1 - 6 ). IEEE . Search in Google Scholar

[14] Din, G. M. U., & & Marnerides, A. K. (2017, January). Short term power load forecasting using deep neural networks. In 2017 International conference on computing, networking and communications (ICNC) (pp. 594-598). IEEE. Din G. M. U. Marnerides A. K. ( 2017 , January ). Short term power load forecasting using deep neural networks . In 2017 International conference on computing, networking and communications (ICNC) (pp. 594 - 598 ). IEEE . Search in Google Scholar

[15] Savari, G. F., Krishnasamy, V., Sathik, J., Ali, Z. M., & Aleem, S. H. A (2020). Internet of Things based real-time electric vehicle load forecasting and charging station recommendation. ISA transactions, 97, 431-447. Savari G. F. Krishnasamy V. Sathik J. Ali Z. M. Aleem S. H. A. ( 2020 ). Internet of Things based real-time electric vehicle load forecasting and charging station recommendation . ISA transactions , 97 , 431 - 447 . Search in Google Scholar

[16] Amarasinghe, K., Marino, D. L., & Manic, M. (2017, June). Deep neural networks for energy load forecasting. In 2017 IEEE 26th international symposium on industrial electronics (ISIE) (pp. 1483-1488). IEEE. Amarasinghe K. Marino D. L. Manic M. ( 2017 , June ). Deep neural networks for energy load forecasting . In 2017 IEEE 26th international symposium on industrial electronics (ISIE) (pp. 1483 - 1488 ). IEEE . Search in Google Scholar

[17] Alhussein, M., Aurangzeb, K., & Haider, S. I. (2020). Hybrid CNN-LSTM model for short-term individual household load forecasting. Ieee Access, 8, 180544-180557. Alhussein M. Aurangzeb K. Haider S. I. ( 2020 ). Hybrid CNN-LSTM model for short-term individual household load forecasting . Ieee Access , 8 , 180544 - 180557 . Search in Google Scholar

[18] Ma, W., Fang, S., Liu, G., & Zhou, R. (2017). Modeling of district load forecasting for distributed energy system. Applied Energy, 204, 181-205. Ma W. Fang S. Liu G. Zhou R. ( 2017 ). Modeling of district load forecasting for distributed energy system . Applied Energy , 204 , 181 - 205 . Search in Google Scholar

[19] He, W. (2017). Load forecasting via deep neural networks. Procedia Computer Science, 122, 308-314. He W. ( 2017 ). Load forecasting via deep neural networks . Procedia Computer Science , 122 , 308 - 314 . Search in Google Scholar

[20] Qi, L., Dou, W., Wang, W., Li, G., Yu, H., & Wan, S. (2018). Dynamic mobile crowdsourcing selection for electricity load forecasting. IEEE access, 6, 46926-46937. Qi L. Dou W. Wang W. Li G. Yu H. Wan S. ( 2018 ). Dynamic mobile crowdsourcing selection for electricity load forecasting . IEEE access , 6 , 46926 - 46937 . Search in Google Scholar

[21] Zhan Shi & Yutu Liang. (2024). An optimized compression algorithm for distributed photovoltaic data. Journal of Physics: Conference Series(1),012108-012108. Shi Zhan Liang Yutu ( 2024 ). An optimized compression algorithm for distributed photovoltaic data . Journal of Physics: Conference Series ( 1 ), 012108 - 012108 . Search in Google Scholar

[22] Zaheda Sultana,CH Hussaian Basha,Mujahid Irfan Mohammed & Sujata Shivashimpiger. (2024). A Novel development of soft computing based hybrid power point tracking controllers for hydrogen vehicle application with new wide source DC-DC converter. Results in Engineering103349-103349. Sultana Zaheda Basha CH Hussaian Mohammed Mujahid Irfan Shivashimpiger Sujata ( 2024 ). A Novel development of soft computing based hybrid power point tracking controllers for hydrogen vehicle application with new wide source DC-DC converter . Results in Engineering 103349 - 103349 . Search in Google Scholar

[23] Jing Su & Jianmin Liang,Jiayi Zhu & Yongjiang Li. (2024). HCAM-CL: A Novel Method Integrating a Hierarchical Cross-Attention Mechanism with CNN-LSTM for Hierarchical Image Classification. Symmetry(9),1231-1231. Su Jing Liang Jianmin Zhu Jiayi Li Yongjiang ( 2024 ). HCAM-CL: A Novel Method Integrating a Hierarchical Cross-Attention Mechanism with CNN-LSTM for Hierarchical Image Classification . Symmetry ( 9 ), 1231 - 1231 . Search in Google Scholar

[24] R. Geethanjali & A. Valarmathi. (2024). A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media. Scientific Reports(1),22270-22270. Geethanjali R. Valarmathi A. ( 2024 ). A novel hybrid deep learning IChOA-CNN-LSTM model for modality-enriched and multilingual emotion recognition in social media . Scientific Reports ( 1 ), 22270 - 22270 . Search in Google Scholar