Abstract
The prediction input variables of the thermal rating (TR) prediction model are commonly historical TRs and relevant meteorological data. However, compared with meteorological elements, intermediate variables obtained in the calculation of the TR including convective heat loss, radiated heat loss and solar heat gain usually have a stronger correlation with the TR. Therefore, this paper proposes a TR probability prediction method with improved prediction input, replacing meteorological elements with intermediate variables as input variables of the quantile regression neural network (QRNN) model to perform TR probability prediction. Finally, the prediction simulation is also conducted to validate the performance of the TR prediction method with improved prediction input.
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.
