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Paper Details
Paper Title
Power System Short-Term Load Forecasting Using Artificial Neural Networks
Authors
Dr. Hassan Kuhba, Hassan A. Hassan Al-Tamemi
Abstract
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.
Keywords- Term Electrical Load Forecasting (STLF), Artificial Neural Networks, Back propagation, Multi-Layer perceptron.
Publication Details
Unique Identification Number - IJEDR1602012Page Number(s) - 78-87Pubished in - Volume 4 | Issue 2 | April 2016DOI (Digital Object Identifier) - Publisher - IJEDR (ISSN - 2321-9939)
Cite this Article
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 (IJEDR), ISSN:2321-9939, Volume.4, Issue 2, pp.78-87, April 2016, Available at :http://www.ijedr.org/papers/IJEDR1602012.pdf
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