ManuScript Details
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Paper Id:
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IJCIRAS1584
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Title:
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ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING
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| Published in: |
International Journal Of Creative and Innovative Research In All Studies |
| Publisher: |
IJCIRAS |
| ISSN: |
2581-5334 |
| Volume / Issue: |
Volume 2 Issue 12 |
| Pages: |
7
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| Published On: |
5/25/2020 1:01:24 AM (MM/dd/yyyy) |
| PDF Url: |
http://www.ijciras.com/PublishedPaper/IJCIRAS1584.pdf |
Main Author Details
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Name:
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Yan Naung Soe |
| Institute: |
Lectruer , University of Computer Studies,Myitkyina |
Co - Author Details
| Author Name |
Author Institute |
| khet khet khaing oo |
Lectruer , University of Computer Studies,Myitkyina |
Abstract
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Research Area:
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Computer Science & Engineering |
| KeyWord: |
Electricity Load Forecasting, Short Term Load Forecasting, Artificial Neural Networks, ANN |
| Abstract: |
The term load prediction refers to the projected load requirement using systematic process of defining load in sufficient quantitative detail so that vital power system expansion decisions can be made. In recent years, there is an emphasis on Short-Term Load Forecasting (STLF), the essential part of power system planning and operation. Rudimentary operating functions such as unit commitment, economic transmit, and unit preservation can be performed efficiently with a precise forecast. Short-term forecasting can assist in predicting the flow and making decisions that prevent overloading. This paper implements the STLF as a 24-hour forecast whose result is an hourly electric forecast. This paper uses the method of Artificial Neural Network (ANN) to create a STLF process. The inputs to the ANN are load profiles of one month previous days and the weather variables of that days. Correlation analysis between load and weather variables will be used for all predictor input data to the ANN to optimize in size and accuracy. MATLAB programming language is used to implement this system. |
Citations
Copy and paste a formatted citation or use one of the links to import into a bibliography manager and reference.
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IEEE
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Yan Naung Soe, khet khet khaing oo, "ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING", International Journal Of Creative and Innovative Research In All Studies,
vol. 2, no. 12, pp. 88-94, 2020.
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MLA
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Yan Naung Soe, khet khet khaing oo "ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING." International Journal Of Creative and Innovative Research In All Studies,
vol 2, no. 12, 2020, pp. 88-94.
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APA
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Yan Naung Soe, khet khet khaing oo (2020). ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING. International Journal Of Creative and Innovative Research In All Studies,
2(12), 88-94.
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ARTIFICIAL NEURAL NETWORKS APPROACH BASED SHORT TERM ELECTRIC LOAD FORECASTING
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Last downloaded on 09/07/2020
