Modeling of Short Term Load Forecasting for Khartuom State Using ANN

Electric load forecasting is the process used to forecast future electric load, given historical load, weather information and current weather information. This work developed model for STLF using Artificial Neural Network (ANNs) approach. Artificial Neural Network (ANN) method is applied to forecast the short-term load for Khartoum State. A nonlinear load model for the load is proposed and several structures of ANN for short-term load forecasting are tested. Inputs to the ANN are past loads and the output of the ANN is the load forecast for a given day. The network with one hidden layer is tested with various combinations of neurons, and results are compared in terms of forecasting error. The model, when tested for seven random days, gives average percentage error of 3.11%.

About the author

Ashraf Mohammed Adam MusaBorn 1984, Sudan B.Sc. Electrical engineering, University of Khartoum. 2008M.Sc. Electrical engineering, University of Khartoum.2014Lecturer in many universities in Sudan.

Product details

• Authors: Ashraf Musa
• Publisher: LAP Lambert Academic Publishing
• Languages: English, German
• Product format: Paperback / Softback
• Released: 07.07.2017
• EAN: 9783659851124
• ISBN: 978-3-659-85112-4
• No. of pages: 96
• Subject: Natural sciences, medicine, IT, technology > Physics, astronomy > Electricity, magnetism, optics