Abstract
Renewable energy sources (RES) are sources of electricity production with a sharply uneven load coverage schedule. Unsatisfactory accuracy of RES forecasts leads to additional production/consumption imbalances and additional costs for settling these imbalances. The need to unload or load flexible electricity producers to compensate for the potential imbalance created by electricity producers at the feed-in tariff will lead to a significant increase in the cost of electricity in such organized market segments as Balancing Market and Ancillary services market. Created imbalances of RES will lead to an even greater increase in electricity prices in terms of the transmission tariff of the Transmission System Operator due to both the payment of electricity produced for feed-in and the increase of imbalances created by them. In such conditions, you can improve the quality of decision-making when planning regimes using accurate forecasts for day-ahead and intraday markets. Statistical methods for forecasting time series have become widespread in the problem of forecasting the RES energy generation. The essence of the methods of this class is to select the parameters of statistical models by minimizing the error of the forecast on historical data. The simplest are one-factor models that take into account only the previous values of generated energy volume. In this class, the most common models are ARIMA (Box-Jenkins in various modifications), exponential smoothing (Holt-Winters in various modifications), and models based on the Kalman filter. The development of machine learning theory and artificial intelligence has led to effective mathematical tools for constructing complex hierarchical models such as support vector machines (SVMs) and artificial neural networks (ANNs) including deep learning methods. The advantages of models of this class are their flexibility, high generalization ability (the ability to make accurate predictions on data that are not in the learning process), and the ability to work with data without prior selection of significant features by an expert. Usually for a neural network of deep learning requires much more data than for classical models. Their disadvantages include the complexity of developing a network architecture and calibration of the learning process. By choosing successful architecture and learning procedure parameters, it is possible to fully automate the neural network learning process for new objects and adapt to changes in existing data. In this chapter, we propose a novel architecture for short-term energy forecasting of aggregated RES generation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Lazard. Levelized Cost of Energy and Levelized Cost of Storage—2020. https://www.lazard.com/perspective/levelized-cost-of-energy-and-levelized-cost-of-storage-2020/
IRENA. Renewable Power Generation Costs in 2019. https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019
CEL. LCOE of renewable energy sources in Ukraine. https://cel.com.ua/reports/lcoe-ukraine-2019/
OECD. The Organisation for Economic Co-operation and Development. https://stats.oecd.org/Index.aspx?DataSetCode=RE_FIT
Kyrylenko, O.V., Basok, B.I., Baseyev, Y., Blinov, I.V.: Power industry of Ukraine and realities of the global warming. Technical Electrodynamics 3—C, 52–61 (2020). DOI: https://doi.org/10.15407/techned2020.03.052
Kulyk, M., Zgurovets, O.: Modeling of power systems with wind, solar power plants and energy storage. Part of the Book Studies in Systems, Decision and Control book series (SSDC, volume 298), pp. 231–245. Springer. (2020). DOI:https://doi.org/10.1007/978-3-030-48583-2_15
Ivanov, H., Blinov, I., Parus, Y.: Simulation model of new electricity market in Ukraine. IEEE 6th International Conference on Energy Smart Systems. (2019). DOI:https://doi.org/10.1109/ESS.2019.8764184
Liu, H., Chen, C., Lv, X., Wu, X., Liu, M.: Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods. Energy Convers. Manage. 195, 328–345 (2019)
Dybowski, R., Roberts, S.: Confidence intervals and prediction intervals for feed-forward neural networks. Clinical Applications of Artificial Neural Networks, pp 298–326. U.K. Cambridge University Press (2000)
Chernenko, P., Miroshnyk, V.: Short-term electrical load forecasting for the electrical supply company with deep neural network. The proceedings of the Institute of Electrodynamics of the National Academy of Sciences of Ukraine, 50, 5–11. (2018). DOI: https://doi.org/10.15407/publishing2018.50.005
Lezhniuk, P., Kravchuk, S., Netrebskiy, V., Komar, V., Lesko, V.: Forecasting hourly photovoltaic generation on day ahead. 2019 IEEE 6th International Conference on Energy Smart Systems (ESS). DOI:https://doi.org/10.1109/ESS.2019.8764245
Yuan, K., Zhang, K., Zheng, Y., Li, D., Wang, Y., Yang, Z.: Irregular distribution of wind power prediction. J. Modern Power Syst. Clean Energy 6, 1172–1180 (2018). https://doi.org/10.1007/s40565-018-0446-9
Nazir, M.S., Alturise, F., Alshmrany, S., Nazir, H.M.J, Bilal, M., Abdalla, A.N., Sanjeevikumar, P., Ali, Z.M.: Wind generation forecasting methods and proliferation of artificial neural network: a review of five years research trend. Sustainability 12 (2020). DOI:https://doi.org/10.3390/su12093778
Chen, Q., Folly, K.A.: Wind power forecasting. IFAC-Papers On Line 51(28), 414–441. (2018). DOI: https://doi.org/10.1016/j.ifacol.2018.11.738
Nguyen, X.H., Nguyen, M.P.: Mathematical modeling of photovoltaic cell/module/arrays with tags in Matlab/Simulink. Environ. Syst. Res. 4(24). (2015). DOI https://doi.org/10.1186/s40068-015-0047-9
Tsai, H.-L., Tu, C.-S., Su, Y.-J.: Development of generalized photovoltaic model using MATLAB/Simulink. Proceedings of the World Congress on Engineering and Computer Science, San Francisco, USA (2008).
Manero, J., B´ejar, J., Cort´es, U.: Deep learning is blowing in the wind. Deep models applied to wind prediction at turbine level. Journal of Physics: Conference Series, vol.1222, IOP Publishing, Spain (2019). DOI:https://doi.org/10.1088/1742-6596/1222/1/012037
Huang, Y., Li, J., Hou, W., Zhang, B., Zhang, Y., Li, Y., Sun, L.: Improved clustering and deep learning based short-term wind energy forecasting in large-scale wind farms. J. Renew. Sustain. Energy 12(6). (2020). DOI: https://doi.org/10.1063/5.0016226
Chen, L., Li, Z., Zhang, Y.: Multiperiod-ahead wind speed forecasting using deep neural architecture and ensemble learning. Hindawi Mathem. Probl. Eng. (2019). https://doi.org/10.1155/2019/9240317
Qureshi, A.S., Khan, A.: Adaptive transfer learning in deep neural networks: wind power prediction using knowledge transfer from region to region and between different task domains. Comput. Intell. 35(4), 1088–1112 (2019). https://doi.org/10.1111/coin.12236
Qureshi, A.S., Khan, A., Zameer, A., Usman, A.: Wind power prediction using deep neural network based meta regression and transfer learning. Appl. Soft Comput. 58, 742–755 (2017). https://doi.org/10.1016/j.asoc.2017.05.031
Torres, J., Aguilar, R.M., Zúñiga, K.V.: Deep learning to predict the generation of a wind farm. J. Renew. Sustain. Energy 10(1). (2018). DOI: https://doi.org/10.1063/1.4995334
AlKandari, M., Ahmad, I.: Solar power generation forecasting using ensemble approach based on deep learning and statistical methods. Appl. Comput. Infor. (2020). https://doi.org/10.1016/j.aci.2019.11.002
Assimakopoulos, V., Nikolopoulos, K.: The theta model: a decomposition approach to forecasting. Int. J. Forecasting 16(4), 521–530 (2000)
Gensler, A., Henze, J., Sick, B., Raabe, N.: Deep learning for solar power forecasting—an approach using AutoEncoder and LSTM Neural Networks. IEEE International Conference on Systems, Man, and Cybernetics 2016, Budapest, Hungary. (2016). DOI: https://doi.org/10.1109/SMC.2016.7844673
Torres, J.F., Troncoso, A., Koprinska, I., Wang, Z., Martínez-Álvarez, F.: Big data solar power forecasting based on deep learning and multiple data sources. Exp. Syst. 36(4). (2019). DOI: https://doi.org/10.1111/exsy.12394
Mishra, S., Palanisamy, P.: Multi-time-horizon solar forecasting using recurrent neural network. 2018 IEEE Energy Conversion Congress and Exposition, Portland, USA. (2018). DOI: https://doi.org/10.1109/ECCE.2018.8558187
Ivanov, H.A., Blinov, I.V., Parus, E.V., Miroshnyk, V.O.: Components of model for analysis of influence of renewables on the electricity market price in Ukraine. Tekhnichna Elektrodynamika 4, 72–75. (2020). DOI: https://doi.org/10.15407/techned2020.04.072
Karp, I.M., Nikitin, Y.Y., Pyanykh, K.Y.: Renewable sources in the energy supply systems of Ukrainian cities. Tekhnichna Elektrodynamika 1, 40–49. (2021). DOI: https://doi.org/10.15407/techned2021.01.040
Agamalov, O.: Grid-forming/following control for converters of renewable energy sources. Tekhnichna Elektrodynamika 1, 50–52. (2021). DOI: https://doi.org/10.15407/techned2021.01.050
Shavelkin, A., Shvedchykova, I.: Management of generation and redistribution electric power in grid-tied photovoltaic system of local object. Tekhnichna Elektrodynamika 4, 55–59 (2020). DOI: https://doi.org/10.15407/techned2020.04.055
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Miroshnyk, V., Shymaniuk, P., Sychova, V. (2022). Short Term Renewable Energy Forecasting with Deep Learning Neural Networks. In: Kyrylenko, O., Zharkin, A., Butkevych, O., Blinov, I., Zaitsev, I., Zaporozhets, A. (eds) Power Systems Research and Operation. Studies in Systems, Decision and Control, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-030-82926-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-82926-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82925-4
Online ISBN: 978-3-030-82926-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)