Abstract
Solar radiation directly affects human health and the surrounding environment. Therefore, scientists are paying much attention to this aspect to control the level of radiation. This paper introduces a new model to predict solar radiation using the collected dataset. Our approach focuses on predicting solar radiation frequency with a deep-learning network model. Instead of ideas directly indicating the outcome with one regression model (deep learning or machine learning), we take inspiration from the saying “divide and conquer” to propose a layered learning model. We implement classification models before building local regression models for classes. Our proposal obtains the expected results with \(99\%\) accuracy for the classification and an MAE of 17.8556 for the regression model. In this paper, we also compare our approach with existing models. Two highlights are: (1) our model is better than several approaches, and (2) it forecasts the ability of solar radiation in the next fifteen minutes based on the current information/data.
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References
Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.: A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev. 124, 109792 (2020)
Aloysius, N., Geetha, M.: A review on deep convolutional neural networks. In: 2017 International Conference on Communication and Signal Processing (ICCSP), pp. 0588–0592. IEEE (2017)
Alqudah, M., Dokic, T., Kezunovic, M., Obradovic, Z.: Prediction of solar radiation based on spatial and temporal embeddings for solar generation forecast. arXiv preprint arXiv:2206.08832 (2022)
Benjamin, M.A., Rigby, R.A., Stasinopoulos, D.M.: Generalized autoregressive moving average models. J. Am. Stat. Assoc. 98(461), 214–223 (2003)
Bodansky, D.: The Copenhagen climate change conference: a postmortem. Am. J. Int. Law 104(2), 230–240 (2010)
Boland, J.: Spatial-temporal forecasting of solar radiation. Renew. Energy 75, 607–616 (2015)
Boubaker, S., Benghanem, M., Mellit, A., Lefza, A., Kahouli, O., Kolsi, L.: Deep neural networks for predicting solar radiation at Hail Region, Saudi Arabia. IEEE Access 9, 36719–36729 (2021)
Box, G.E., Pierce, D.A.: Distribution of residual autocorrelations in autoregressive-integrated moving average time series models. J. Am. Stat. Assoc. 65(332), 1509–1526 (1970)
Das, U.K., et al.: Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sustain. Energy Rev. 81, 912–928 (2018)
De Myttenaere, A., Golden, B., Le Grand, B., Rossi, F.: Mean absolute percentage error for regression models. Neurocomputing 192, 38–48 (2016)
Do, T.N.: Training neural networks on top of support vector machine models for classifying fingerprint images. SN Comput. Sci. 2(5), 355 (2021). https://doi.org/10.1007/s42979-021-00743-0
Do, T.-N., Pham, T.-P., Pham, N.-K., Nguyen, H.-H., Tabia, K., Benferhat, S.: Stacking of SVMs for classifying intangible cultural heritage images. In: Le Thi, H.A., Le, H.M., Pham Dinh, T., Nguyen, N.T. (eds.) ICCSAMA 2019. AISC, vol. 1121, pp. 186–196. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38364-0_17
Do, T.N., Pham, T.P., Tran-Nguyen, M.T.: Fine-tuning deep network models for classifying fingerprint images. In: 2020 12th International Conference on Knowledge and Systems Engineering (KSE), pp. 79–84. IEEE (2020)
Dong, L.: The trump administration’s decision to withdraw the United States from the Paris Climate Agreement. Chin. J. Popul. Resour. Environ. 15(3), 183 (2017)
Du, J., Xu, Y.: Hierarchical deep neural network for multivariate regression. Pattern Recogn. 63, 149–157 (2017)
Gupta, P., Singh, R.: PV power forecasting based on data-driven models: a review. Int. J. Sustain. Eng. 14(6), 1733–1755 (2021)
Gutierrez-Corea, F.V., Manso-Callejo, M.A., Moreno-Regidor, M.P., Manrique-Sancho, M.T.: Forecasting short-term solar irradiance based on artificial neural networks and data from neighboring meteorological stations. Sol. Energy 134, 119–131 (2016)
Herzog, A.V., Lipman, T.E., Kammen, D.M., et al.: Renewable energy sources. In: Encyclopedia of Life Support Systems (EOLSS). Forerunner Volume-Perspectives and Overview of Life Support Systems and Sustainable Development, vol. 76 (2001)
Kabir, E., Kumar, P., Kumar, S., Adelodun, A.A., Kim, K.H.: Solar energy: potential and future prospects. Renew. Sustain. Energy Rev. 82, 894–900 (2018)
Kattenborn, T., Leitloff, J., Schiefer, F., Hinz, S.: Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J. Photogramm. Remote. Sens. 173, 24–49 (2021)
Kumari, P., Toshniwal, D.: Long short term memory-convolutional neural network based deep hybrid approach for solar irradiance forecasting. Appl. Energy 295, 117061 (2021)
Li, P., Zhou, K., Lu, X., Yang, S.: A hybrid deep learning model for short-term PV power forecasting. Appl. Energy 259, 114216 (2020)
Long, H., Zhang, Z., Su, Y.: Analysis of daily solar power prediction with data-driven approaches. Appl. Energy 126, 29–37 (2014)
Lorenc, A.C.: Analysis methods for numerical weather prediction. Q. J. R. Meteorol. Soc. 112(474), 1177–1194 (1986)
Lupangu, C., Bansal, R.: A review of technical issues on the development of solar photovoltaic systems. Renew. Sustain. Energy Rev. 73, 950–965 (2017)
Mascarenhas, S., Agarwal, M.: A comparison between VGG16, VGG19 and ResNet50 architecture frameworks for Image Classification. In: 2021 International Conference on Disruptive Technologies for Multi-Disciplinary Research and Applications (CENTCON), vol. 1, pp. 96–99. IEEE (2021)
Miller, D.J., Xiang, Z., Kesidis, G.: Adversarial learning targeting deep neural network classification: a comprehensive review of defenses against attacks. Proc. IEEE 108(3), 402–433 (2020)
Onim, M.S.H., et al.: SolNet: a convolutional neural network for detecting dust on solar panels. Energies 16(1), 155 (2022)
Phan, A.C., Nguyen, N.H.Q., Trieu, T.N., Phan, T.C.: An efficient approach for detecting driver drowsiness based on deep learning. Appl. Sci. 11(18), 8441 (2021)
Prusty, S., Patnaik, S., Dash, S.K.: ResNet50V2: a transfer learning model to predict pneumonia with chest X-ray images. In: 2022 International Conference on Machine Learning, Computer Systems and Security (MLCSS), pp. 208–213. IEEE (2022)
Rodríguez, F., Martín, F., Fontán, L., Galarza, A.: Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power. Energy 229, 120647 (2021)
Smith, D.R.: The design of divide and conquer algorithms. Sci. Comput. Program. 5, 37–58 (1985)
Touti, E., Zayed, H., Pusca, R., Romary, R.: Dynamic stability enhancement of a hybrid renewable energy system in stand-alone applications. Computation 9(2), 14 (2021)
Vignola, F., Grover, C., Lemon, N., McMahan, A.: Building a bankable solar radiation dataset. Sol. Energy 86(8), 2218–2229 (2012)
Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017)
Walch, A., Castello, R., Mohajeri, N., Scartezzini, J.L.: A fast machine learning model for large-scale estimation of annual solar irradiation on rooftops. In: Proceedings of Solar World Congress 2019. International Solar Energy Society ISES (2020)
Wang, H., Lei, Z., Zhang, X., Zhou, B., Peng, J.: A review of deep learning for renewable energy forecasting. Energy Convers. Manag. 198, 111799 (2019)
Xu, Y., Du, J., Dai, L.R., Lee, C.H.: A regression approach to speech enhancement based on deep neural networks. IEEE/ACM Trans. Audio Speech Lang. Process. 23(1), 7–19 (2014)
Yuan, C., Marion, T., Moghaddam, M.: Leveraging end-user data for enhanced design concept evaluation: a multimodal deep regression model. J. Mech. Des. 144(2), 021403 (2022)
Zang, H., Liu, L., Sun, L., Cheng, L., Wei, Z., Sun, G.: Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations. Renew. Energy 160, 26–41 (2020)
Zhou, Y., Liu, Y., Wang, D., Liu, X., Wang, Y.: A review on global solar radiation prediction with machine learning models in a comprehensive perspective. Energy Convers. Manag. 235, 113960 (2021)
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Trang, TT., Ma, T., Do, TN. (2023). LORAP: Local Deep Neural Network for Solar Radiation Prediction. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2023. Communications in Computer and Information Science, vol 1925. Springer, Singapore. https://doi.org/10.1007/978-981-99-8296-7_26
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