Forecasting the PV Power Utilizing a Combined Convolutional Neural Network and Long Short-Term Memory Model

References

• S. Li, J. Yang, F. Wu, R. Li, and G. I. Rashed, “Combined prediction of photovoltaic power based on sparrow search algorithm optimized convolution long and short-term memory hybrid neural network,” Electronics., vol. 11, no. 10, pp. 1654, May 2022. DOI: 10.3390/electronics11101654.
   Web of Science ®Google Scholar
• J. Ospina, A. Newaz, and M. O. Faruque, “Forecasting of PV plant output using hybrid wavelet‐based LSTM‐DNN structure model,” IET Renew. Power Gener., vol. 13, no. 7, pp. 1087–1095, May 2019. DOI: 10.1049/iet-rpg.2018.5779.
   Web of Science ®Google Scholar
• X. Chen, Y. Li, Y. Zhang, X. Ye, X. Xiong, and F. Zhang, “A novel hybrid model based on an improved seagull optimization algorithm for short-term wind speed forecasting,” Process., vol. 9, no. 2, pp. 387, Feb. 2021. DOI: 10.3390/pr9020387.
   Web of Science ®Google Scholar
• H. Li, Z. Ren, Y. Xu, L. Wenyuan, and B. Hu, “A multi-data driven hybrid learning method for weekly photovoltaic power scenario forecast,” IEEE Trans. Sustain. Energy., vol. 13, no. 1, pp. 91–100, Jan. 2022. DOI: 10.1109/TSTE.2021.3104656.
   Web of Science ®Google Scholar
• A. S. F. Rocha, F. K. D. O. M. V. Guerra, and M. R. B. G. Vale, “Forecasting the performance of a photovoltaic solar system installed in other locations using artificial neural networks,” Electr. Power Compon. Syst., vol. 48, no. 1–2, pp. 201–212, Jan. 2020. DOI: 10.1080/15325008.2020.1736211.
   Web of Science ®Google Scholar
• S. Ramesh, et al., “Optimization of solar hybrid power generation using conductance-fuzzy dual-mode control method,” Int. J. Photoenergy., vol. 2022, pp. 1–10, 2022. DOI: 10.1155/2022/7756261.
   Web of Science ®Google Scholar
• M. Pan, et al., “Photovoltaic power forecasting based on a support vector machine with improved ant colony optimization,” J. Clean. Prod., vol. 277, pp. 123948, Dec. 2020. DOI: 10.1016/j.jclepro.2020.123948.
   Web of Science ®Google Scholar
• Y. Zhou, N. Zhou, L. Gong, and M. Jiang, “Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine,” Energy., vol. 204, pp. 117894, Aug. 2020. DOI: 10.1016/j.energy.2020.117894.
   Web of Science ®Google Scholar
• H. Liu, Z. Duan, H. Wu, Y. Li, and S. Dong, “Wind speed forecasting models based on data decomposition, feature selection and group method of data handling network,” Meas., vol. 148, pp. 106971, Dec. 2019. DOI: 10.1016/j.measurement.2019.106971.
   Web of Science ®Google Scholar
• Y. Ju, J. Li, and G. Sun, “Ultra-short-term photovoltaic power prediction based on self-attention mechanism and multi-task learning,” IEEE Access., vol. 8, pp. 44821–44829, 2020. DOI: 10.1109/ACCESS.2020.2978635.
   Google Scholar
• T. M. Amirthalakshmi, et al., “A novel approach in hybrid energy storage system for maximizing solar PV energy penetration in microgrid,” Int. J. Photoenergy., vol. 2022, pp. 1–7, 2022. DOI: 10.1155/2022/3559837.
   Web of Science ®Google Scholar
• A. Mellit, A. M. Pavan, and V. Lughi, “Deep learning neural networks for short-term photovoltaic power forecasting,” Renew. Energy., vol. 172, pp. 276–288, Jul. 2021. DOI: 10.1016/j.renene.2021.02.166.
   Web of Science ®Google Scholar
• M. Tovar, M. Robles, and F. Rashid, “PV power prediction, using CNN-LSTM hybrid neural network model. Case of study: Temixco-Morelos, México,” Energies., vol. 13, no. 24, pp. 6512, Dec. 2020. DOI: 10.3390/en13246512.
   Web of Science ®Google Scholar
• M. Aslam, S.-J. Lee, S.-H. Khang, and S. Hong, “Two-stage attention over LSTM with Bayesian optimization for day-ahead solar power forecasting,” IEEE Access., vol. 9, pp. 107387–107398, 2021. DOI: 10.1109/ACCESS.2021.3100105.
   Web of Science ®Google Scholar
• S. Zhou, L. Zhou, M. Mao, and X. Xi, “Transfer learning for photovoltaic power forecasting with long short-term memory neural network,” presented at 2020 IEEE Int. Conf. Big Data and Smart Comput. (BigComp), pp. 125–132, Feb. 2020. DOI: 10.1109/BigComp48618.2020.00-87.
   Google Scholar
• H. Aprillia, H. T. Yang, and C. M. Huang, “Short-term photovoltaic power forecasting using a convolutional neural network–salp swarm algorithm,” Energies., vol. 13, no. 8, pp. 1879, Apr. 2020. DOI: 10.3390/en13081879.
   Web of Science ®Google Scholar
• Q. Li, X. Zhang, T. Ma, C. Jiao, H. Wang, and W. Hu, “A multi-step ahead photovoltaic power prediction model based on similar day, enhanced colliding bodies optimization, variational mode decomposition, and deep extreme learning machine,” Energy., vol. 224, pp. 120094, Jun. 2021. DOI: 10.1016/j.energy.2021.120094.
   Web of Science ®Google Scholar
• Y. Liu, W. Wang, and N. Ghadimi, “Electricity load forecasting by an improved forecast engine for building level consumers,” Energy, vol. 139, pp. 18–30, Nov. 2017. DOI: 10.1016/j.energy.2017.07.150.
   Web of Science ®Google Scholar
• J. Xia, Y. Wang, and Y. Li, “A navigation satellite selection method based on tabu search artificial bee colony algorithm,” presented at 2020 IEEE 3rd Int. Conf. Elect. Info. Commun. Tech. (ICEICT), pp. 421–425, Nov. 2020. DOI: 10.1109/ICEICT51264.2020.9334301.
   Google Scholar
• M. Cinar and A. Kaygusuz, “Optimum fuel cost in load flow analysis of smart grid by using artificial bee colony algorithm,” presented at 2019 Int. Artif. Intell. Data Process. Sympos. (IDAP), pp. 1–5, Sep. 2019. DOI: 10.1109/IDAP.2019.8875893.
   Google Scholar
• H. T. C. Pedro, D. P. Larson, and C. F. M. Coimbra, “A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods,” J. Renew. Sustain. Energy., vol. 11, no. 3, pp. 036102, May 2019. DOI: 10.1063/1.5094494.
   Web of Science ®Google Scholar
• K. Kaysal, F. O. Hocaoğlu, and N. Öztürk, “Comparison the performance of different optimization methods in artificial intelligence based electricity production forecasting,” presented at 2022 10th icSmartGrid., pp. 236–239, 2022. DOI: 10.1109/icSmartGrid55722.2022.9848724.
   Google Scholar
• I. Bodur, E. Celik, and N. Ozturk, “A short-term load demand forecasting based on the method of LSTM,” presented at 2021 10th ICRERA., pp. 171–174, 2021. DOI: 10.1109/ICRERA52334.2021.9598773.
   Google Scholar
• A. Rajaram and K. Sathiyaraj, “An improved optimization technique for energy harvesting system with grid connected power for green house management,” J. Electr. Eng. Technol., vol. 17, no. 5, pp. 2937–2949, Jun. 2022. DOI: 10.1007/s42835-022-01033-2.
   Web of Science ®Google Scholar