DETERMINATION OF ELECTRICAL LOSSES BASED ON NODAL ELECTRICAL LOAD FORECASTS

Authors

DOI:

https://doi.org/10.20535/1813-5420.3.2022.271484

Keywords:

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

Abstract

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.

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Published

2023-03-09

Issue

Section

ENERGY SYSTEMS AND COMPLEXES