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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Changhao Xia Zhonghua Yang Hongjie Li |
| Copyright Year | 2012 |
| Description | Author affiliation: Coll. of Electr. Eng. & Renewable Energy, China Three Gorges Univ., Yichang, China (Changhao Xia; Zhonghua Yang; Hongjie Li) |
| Abstract | In order to improve accuracy of load forecasting for power grid, since the load characteristics of Yichang power grid is sensitive to climate impact, an Elman neural network (NN)-based short-term load forecasting model under comprehensive consideration of various meteorological factors is established. Elman NN has a dynamic recurrent performance which is able to enhance the adaptability of forecasting model. Actual historical hourly loads and weather data of Yichang city are used to build training sample set for NN. The simulation results indicate that the model based on Elman NN has a higher accuracy. Using the method of LabVIEW calling MATLAB, the NN load forecasting model was implanted in and a Virtual Instrument (VI) for load forecasting has been designed. Inputting meteorological factors such as temperature, precipitation, the VI can output load curve, error curve, maximum, minimum and average load. The VI is easy to implement and intuitive. The result shows the effectiveness of this load forecasting method which can be used in practical application. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 548947 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781457716003 |
| DOI | 10.1109/PEAM.2012.6612460 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-09-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Power system Load forecasting Artificial neural networks Predictive models Virtual instrument Mathematical model Weather fator Load modeling Elman neural network |
| Content Type | Text |
| Resource Type | Article |
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