Clean Power

Ukrainian (UA)English (United Kingdom)

The National Academy of Sciences of Ukraine

The Institute of Electrodynamics

About Institute



I. Blinov*, V. Miroshnyk, P. Shymaniuk
Institute of Electrodynamics of the National Academy of Sciences of Ukraine,
Peremohy, 56, Kyiv-57, 03680, Ukraine,
e-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it

The paper proposes the architecture of deep learning artificial neural network for short-term forecasting of total electricity supply by renewable energy sources (RES) producers. The paper shown that using such neural network, it is advisable to predict 10 and 90 percentile of error distribution, which gives the lower and upper bounds of the forecast interval with a hit probability of 0.8 in addition to point forecasting of the most probable value of electricity release. An error function is used for training, which is a combination of the mean squared error deviation and the quantile regression error for the percentile model. The test of the model was carried out on the real data of the total release of RES producers published by SE “Energorynok”. The quality of the forecast was compared with forecast data of the manufacturers. The minimum average error is reached by combination of neural network and manufacturers' forecasts. The lowest maximum error is provided by the independent neural network forecast. The forecast interval provides a probability of 0.82 for getting into this interval of actual values with an expected value of 0.8. References 9, figures 4, table 1.
Key words: renewable sources, electricity market, short-term forecasting, forecast interval, deep learning neural networks.

1. Blinov I.V., Parus Ye.V., Ivanov H.A. Imitation modeling of the balancing electricity market functioning taking into account system constraints on the parameters of the ips of Ukraine mode. Tekhnichna Elektrodynamika. 2017. No 6. Pp. 72–79. DOI:
2. Kutsan Yu.H., Blinov I.V., Ivanov H.A. Modelling of Tariff and Price Formation on Retain Market of Electrical Energy of Ukraine in New Conditions of its Functioning. Electronic modeling. 2017. Vol. 39 (5). Pp. 71–80. DOI:
3. Croonenbroeck C., Stadtmann G. Renewable generation forecast studies–Review and good practice guidance. Renewable and Sustainable Energy Reviews. 2019. Vol. 108. Pp. 312–322. DOI:
4. Liu H., Chen C., Lv X., Wu X., Liu M. Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods. Energy Conversion and Management. 2019. Vol. 195. Pp. 328–345. DOI:
5. Mellit, A., & Kalogirou, S. A. Artificial intelligence techniques for photovoltaic applications. A review. Progress in energy and combustion science. 2008. Vol. 34, I. 5. Pp. 574–632. DOI:
6. Khosravi A., Nahavandi S., Creighton D. Comprehensive Review of Neural Network-Based Prediction Intervals and New Advances. IEEE Transactions on neural networks. 2011. Vol. 22. I. 9. Pp. 1341–1356. DOI:
7. Dybowski R, Roberts S. Confidence intervals and prediction intervals for feed-forward neural networks. Clinical Applications of Artificial Neural Networks. 2000.
8. Chernenko P.O., Miroshnyk V.O. Short-term electrical load forecasting for electrical supply company with deep neural network. Pratsi Instytutu Elektrodynamiky Natsionalnoi Akademii Nauk Ukrainy. 2018. No 50. Pp. 5–11. DOI:
9. Smith, S. L., Kindermans, P. J., Ying, C.,Le, Q. V. Don't decay the learning rate, increase the batch size. 2017. arXiv preprint arXiv:1711.00489.

Received 07.06.2019