Abstract
There are incredibly renewable outlets such as solar and wind intermittent, and machine reliability is hard to sustain with the intolerable balance of green energy resources. Because of population growth and technological development, global energy demand is increasing exponentially. However, civilization must address a stable energy source for two benefits, a cost-effective and sustainable basis for future renewable energy production. Solar Energy is a platform for green energy and is a potential means of solving problems that face a possible energy system. The power output is primarily determined by the properties of the incoming radiation and solar panels. Precise and accurate knowledge of these variables provides a consistent model for predicting the solar future. Solar power forecasts would have a significant effect on the viability of massive solar energy projects. This study presents a new approach to predict the expected amount of energy to be produced from Photovoltaics using a hybrid deep neural network based on a combination of extended short-term memory networks (LSTM) and convolutional neural networks (CNN), taking into account the energy of electrons and photons as well as elements and climatic conditions as input data.
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References
Awan, S.M., Khan, Z.A., Aslam, M.: Solar generation forecasting by recurrent neural networks optimized by Levenberg-Marquardt Algorithm. In: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, Washington, DC, 2018, pp. 276–281. https://doi.org/10.1109/IECON.2018.8591799
Publishing, O.: Trends in photovoltaic applications 2018, International Energy Agency, Technical rep (2018)
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Future Gen. Comput. Syst. 108, 445–453 (2020). https://doi.org/10.1016/j.future.2020.03.006
Chu, K.C., Horng, D.J., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. J. IEEE Access, 7, 105562–105571 (2019)
AlKandari, M., Ahmad, I.: Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Appl. Comput. Inform. https://doi.org/10.1016/j.aci.2019.11.002
Lu, J., Zhang, Q., Yang, Z., Tu, M.: A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting. In: 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, pp. 1–5 (2019). https://doi.org/10.1109/PESGM40551.2019.8973549
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Energy saving technology of 5G base station based on internet of things collaborative control. IEEE Access 8, 32935–32946 (2020). https://doi.org/10.1109/ACCESS.2020.2973648
de Jesús, D.A.R., Mandal, P., Chakraborty, S., Senjyu, T.: Solar PV power prediction using a new approach based on hybrid deep neural network. In: 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA, pp.1--5 (2019). https://doi.org/10.1109/PESGM40551.2019.8974091
Chang, K.C., Pan, J.S., Chu, K.C., Horng, D.J., Jing, H.: Study on information and integrated of MES big data and semiconductor process furnace automation. In: International Conference on Genetic and Evolutionary Computing, pp. 669–678. Springer, Singapore, December 2018
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Chang, K.C., Chu, K.C., Chen, T.L., Lee, Y.L.W., Lin, Y.C., Nguyen, T.T.: Study of the high-tech process mechanical integrity and electrical safety. In 2019 14th International Microsystems, Packaging, Assembly and Circuits Technology Conference (IMPACT), pp. 162–165. IEEE, October 2019
Haque, A.U., Nehrir, M.H., Mandal, P.: Solar PV power generation forecast using a hybrid intelligent approach. In: 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, pp.1–5 (2013). https://doi.org/10.1109/PESMG.2013.6672634
Chang, K.C., Chu, K.C., Lin, Y.C., Sung, T.W., Zhou, Y.W., Pan, J.S.: Study on health protection behavior based on the big data of high-tech factory production line. In International Conference on Genetic and Evolutionary Computing, pp. 571–578. Springer, Singapore, November 2019
Chang, K.C., Chu, K.C., Lin, Y.C., Sung, T.W., Zhou, Y.W., Pan, J.S.: Study on hazardous scenario analysis of high-tech facilities and emergency response mechanism of science and technology parks based on IoT. In: International Conference on Genetic and Evolutionary Computing, pp. 191–199. Springer, Singapore, November 2019
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
DiPietro, R., Hager, G.D.: Deep learning: RNNs and LSTM. Handb. Med. Image Comput. Comput. Assist. Interv. 2020, 503–519 (2020)
Chu, K.C., Chang, K.C., Wang, H.C., Lin, Y.C., Hsu, T.L.: Field-programmable gate array-based hardware design of optical fiber transducer integrated platform. J. Nanoelectr. Optoelectron. 15(5), 663–671 (2020)
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Chang, KC. et al. (2021). Solar PV Power Forecasting Approach Based on Hybrid Deep Neural Network. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_13
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DOI: https://doi.org/10.1007/978-3-030-69717-4_13
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