Power System Short-Term Load Forecasting Using Artificial Neural Networks
- Dr. Hassan Kuhba
- Hassan A. Hassan Al-Tamemi
Term Electrical Load Forecasting (STLF), Artificial Neural Networks, Back propagation, Multi-Layer perceptron.
In this paper, a multi-layer perceptron with back-propagation algorithm as learning strategy is used to train the neural networks. One of the important features of using (MLP) NNs is the weather variation such as temperature, humidity, cloudiness … etc., can be simulated as the most essential parameters that affect on the predicted load. The proposed method, by computation of the predicted loads for different parameters variations, is demonstrated on practical system (Iraqi National Grid, 14 load buses), and tested by 5-busses test system. The results of short-term load forecasting are obtained for on-line applications with high accuracy and reasonable error.
Dr. Hassan Kuhba, Hassan A. Hassan Al-Tamemi. "Power System Short-Term Load Forecasting Using Artificial Neural Networks".INTERNATIONAL JOURNAL OF ENGINEERING DEVELOPMENT AND RESEARCH ISSN:2321-9939, Vol.4, Issue 2, pp.78-87, URL :https://rjwave.org/ijedr/papers/IJEDR1602012.pdf
Volume 4 Issue 2
Pages. 78-87
