Abstract
Load forecasting is an important component in the economic and secure operation of the restructured power system energy management. This paper presents the use of an artificial neural network to half hourly load forecasting and a day ahead load forecasting application. By using historical weather, load consumption, price and calendar data, a multi-layer feed forward (FF) neural network trained with Back propagation (BP) algorithm was developed for the half hour and a day ahead forecasting. The developed algorithm for a day ahead forecasting has been tested with IIT Roorkee campus data. The half hourly forecasting algorithm has been tested with Australian market data. The results of ANN forecasting model is compared with the conventional Multiple Regression (MR) forecasting model.
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.
