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Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks
Abstract
In deregulated electricity markets, short-term load forecasting is important for reliable power system operation, and also significantly affects markets and their participants. Effective forecasting, however, is difficult in view of the complicated effects on load by a variety of factors. This paper presents a similar day-based wavelet neural network method to forecast tomorrow's load. The idea is to select similar day load as the input load based on correlation analysis, and use wavelet decomposition and separate neural networks to capture the features of load at low and high frequencies. Despite of its "noisy" nature, high frequency load is well predicted by including precipitation and high frequency component of similar day load as inputs. Numerical testing shows that this method provides accurate predictions.
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Metadata
- Authors
- Chen, Y., Luh, P. B., Guan, C., Zhao, Y., Michel, L. D., Coolbeth, M. A., Friedland, P. B., & Rourke, S. J.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2010-02
- Volume
- 25
- Issue
- 1
- Pages
- 322-330
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W1964984358
- Cited by
- 444
- References
- 17
- FWCI
- 10.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Chen, Y., Luh, P. B., Guan, C., Zhao, Y., Michel, L. D., Coolbeth, M. A., Friedland, P. B., & Rourke, S. J. (2010). Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2009.2030426
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