Abstract
In recent years the demand for electrical energy has increased significantly. Usually, the electrical grid covers this demand. However, this fuel energy is known for its significant carbon footprint. For that reason, different mechanisms to bring cleaner energies have been explored, like hydraulic, wind, thermal, and one of the most popular solar energy. Although solar energy is abundant and environmentally friendly, the photovoltaic energy that comes from the sun, solar production is subject to different external perturbations, such as environmental conditions. Therefore it has been necessary to develop other methods based on statistics, machine learning, or deep learning to make solar forecasting and predict production and weather conditions. Specifically, this work proposes an evaluation of three different deep learning models to predict irradiance, temperature, and production of a photovoltaic system located in the city of Cartagena, Colombia. Those are irradiance and temperature using the historical data on production and weather conditions. This data has been registered on a web platform for seven months, from January 1, 2022, until June 28, 2022.
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References
Dincer, F.: The analysis on wind energy electricity generation status, potential and policies in the world. Renew. Sustain. Energy Rev. 15(9), 5135–5142 (2011)
Brockway, P.E., Owen, A., Brand-Correa, L.I., Hardt, L.: Estimation of global final-stage energy-return-on-investment for fossil fuels with comparison to renewable energy sources. Nat. Energy 4(7), 612–621 (2019)
Aichele, R., Felbermayr, G.: Kyoto and the carbon footprint of nations. J. Environ. Econ. Manag. 63(3), 336–354 (2012)
Li, K., Bian, H., Liu, C., Zhang, D., Yang, Y.: Comparison of geothermal with solar and wind power generation systems. Renew. Sustain. Energy Rev. 42, 1464–1474 (2015)
Li, G., Li, M., Taylor, R., Hao, Y., Besagni, G., Markides, C.: Solar energy utilisation: current status and roll-out potential. Appl. Thermal Eng. 209, 118285 (2022)
Hafezi, R., Alipour, M.: Renewable energy sources: traditional and modern-age technologies. In: Affordable and Clean Energy, pp. 1085–1099. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-319-95864-4_18
Amant, R.S., Cohen, P.R.: Intelligent support for exploratory data analysis. J. Comput. Graph. Stat. 7(4), 545–558 (1998)
Pezeshki, Z., Mazinani, S.M.: Comparison of artificial neural networks, fuzzy logic and neuro fuzzy for predicting optimization of building thermal consumption: a survey. Artif. Intell. Rev. 52(1), 495–525 (2019)
Li, R., Wang, H.-N., He, H., Cui, Y.-M., Du, Z.-L.: Support vector machine combined with k-nearest neighbors for solar flare forecasting. Chin. J. Astron. Astrophys. 7(3), 441 (2007)
Zhou, Y., Zhou, N., Gong, L., Jiang, M.: Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine. Energy 204, 117894 (2020)
Hocaoğlu, F.O., Gerek, Ö.N., Kurban, M.: Hourly solar radiation forecasting using optimal coefficient 2-d linear filters and feed-forward neural networks. Solar Energy 82(8), 714–726 (2008)
Pang, Z., Niu, F., O’Neill, Z.: Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons. Renew. Energy 156, 279–289 (2020)
Jung, Y., Jung, J., Kim, B., Han, S.: Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar pv facilities: case study of South Korea. J. Cleaner Prod. 250, 119476 (2020)
de Melo, G.A., Sugimoto, D.N., Tasinaffo, P.M., Santos, A.H.M., Cunha, A.M., Dias, L.A.V.: A new approach to river flow forecasting: LSTM and GRU multivariate models. IEEE Latin Am. Trans. 17(12), 1978–1986 (2019)
Gao, B., Huang, X., Shi, J., Tai, Y., Zhang, J.: Hourly forecasting of solar irradiance based on ceemdan and multi-strategy CNN-LSTM neural networks. Renew. Energy 162, 1665–1683 (2020)
Ajith, M., Martínez-Ramón, M.: Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data. Appl. Energy 294, 117014 (2021)
Das, U.K., et al.: Forecasting of photovoltaic power generation and model optimization: a review. Renew. Sustain. Energy Rev. 81, 912–928 (2018)
Lin, P., Peng, Z., Lai, Y., Cheng, S., Chen, Z., Wu, L.: Short-term power prediction for photovoltaic power plants using a hybrid improved kmeans-gra-elman model based on multivariate meteorological factors and historical power datasets. Energy Conv. Manag. 177, 704–717 (2018)
Wang, H., Lei, Z., Zhang, X., Zhou, B., Peng, J.: A review of deep learning for renewable energy forecasting. Energy Conv. Manag. 198, 111799 (2019)
Mellit, A., Pavan, A.M., Ogliari, E., Leva, S., Lughi, V.: Advanced methods for photovoltaic output power forecasting: a review. Appl. Sci. 10(2), 487 (2020)
Li, W., Wu, H., Zhu, N., Jiang, Y., Tan, J., Guo, Y.: Prediction of dissolved oxygen in a fishery pond based on gated recurrent unit (GRU). Inf. Process. Agric. 8(1), 185–193 (2021)
Rajagukguk, R.A., Ramadhan, R.A., Lee, H.-J.: A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power. Energies 13(24), 6623 (2020)
Yang, T., Zhao, L., Li, W., Zomaya, A.Y.: Reinforcement learning in sustainable energy and electric systems: a survey. Ann. Rev. Control 49, 145–163 (2020)
Liu, Y., et al.: Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl. Sci. 9(6), 1108 (2019)
Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
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Martinez, E., Cuadrado, J., Martinez-Santos, J.C. (2022). Photovoltaic Power Predictor Module Based on Historical Production and Weather Conditions Data. In: Figueroa-García, J.C., Franco, C., Díaz-Gutierrez, Y., Hernández-Pérez, G. (eds) Applied Computer Sciences in Engineering. WEA 2022. Communications in Computer and Information Science, vol 1685. Springer, Cham. https://doi.org/10.1007/978-3-031-20611-5_38
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