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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
