nodal electrical load, short-term forecasting, artificial neural network, LSTM, loss, CIGRE.


This study proposed the use of forecasting methods based on artificial neural networks for calculating and forecasting energy losses. The calculation of energy losses was performed on the CIGRE test network. Several approaches were developed to determine energy losses: prediction of electrical energy losses using artificial neural networks, and calculation of losses using nodal load prediction based on artificial neural networks, which were compared with the classical method of calculating losses based on summer and winter peak load coefficients. Depending on forecasting problems, when using artificial neural networks, the amount of electrical energy losses in distribution networks decreased by three times compared to the calculation of losses using coefficients.


Blinov I, Miroshnyk V, Shymaniuk P “The cost of error of" day ahead" forecast of technological losses of electrical energy”, Tekhnichna elektrodynamika, 2020, vol. 5, pp. 70-73. DOI:

Blinov I. Problems of functioning and development of a new electricity market model in Ukraine. Visn. Nac. Acad. Nauk Ukr, 2021, vol. 3. pp. 20-28. DOI:

S. В. Taieb, G. Bontempi, A.F. Atiya, and A. Sorjamaa, “A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition,” Expert Systems with Applications, vol. 39, no. 8, pp. 7067-7083,2012

G. Hou, et al., “A novel algorithm for multi-node load forecasting based on big data of distribution network,” Int. Conf. on Adv.Electron. Sci. and Technol., Shenzhen, 2016, pp 655-667.

X. Wang, N. Hatziargyriou, and L.H. Tsoukalas, “A New Methodology for Nodal Load Forecasting in Deregulated Power Systems,” IEEE Power Engineering Review, vol. 22, pp 48-51, May 2002.

G.P. Shumilova, N.Je. Gotman, and T.B.Starceva, “Prediction of the active and reactive load of EPS units using inversion of an artificial neural network,” Elektrichestvo, vol 6, pp. 7-13, 2007.

P. Shymaniuk, V. Miroshnyk, I. Blinov, P. Chernenko Aspects of temperature taking into account to increase the accuracy of short-term forecasting of node loads. Power engineering: economics, technique, ecology. 2021. Vol. 2. Pp.50-58. DOI:

E. Sbai, and M. Simpson. (2019). “Short-term Forecasting of Nodal Electricity Demand in New Zealand” [Online]. Available:

P. Chernenko V. Miroshnyk P. Shymaniuk “Univariable short-term forecast of nodal electrical loads of energy systems”, Tekhnichna elektrodynamika, 2020, vol. 2, pp. 67-73. DOI:

G. Klambauer, T. Unterthiner, A. Mayr, S. Hochreiter “Self-Normalizing Neural Networks”, Advances in Neural Information Processing Systems, 2017, No 30, pp. 971-980.

V. Miroshnyk, P. Shymaniuk & V. Sychova “Short Term Renewable Energy Forecasting with Deep Learning Neural Networks”, Power Systems Research and Operation, 2021, Pp. 121–142. DOI: 10.1007/978-3-030-82926-1_6

D. Kingma, J. Ba "Adam. A Method for Stochastic Optimization”, Proc. of the 3rd International Conference on Learning Representations (ICLR).

“Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources”, ELECTRA, 2014 DOI:

K. Rudion, A. Orths, Z.A. Styczynski, K. Strunz “Design of benchmark of medium voltage distribution network for investigation of DG integration”, 2006 IEEE Power Engineering Society General Meeting, Montreal, Canada, June 2006. DOI: 10.1109/PES.2006.1709447On Electricyty Market: The Law of Ukraine. No. 2019-VIII of 13.04.2017.