A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting
Journal article
Li, Weide, Kong, Demeng and Wu, Jinran. (2017). A novel hybrid model based on extreme learning machine, k-nearest neighbor regression and wavelet denoising applied to short-term electric load forecasting. Energies. 10(5), p. Article 694. https://doi.org/10.3390/en10050694
Authors | Li, Weide, Kong, Demeng and Wu, Jinran |
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Abstract | Electric load forecasting plays an important role in electricity markets and power systems. Because electric load time series are complicated and nonlinear, it is very difficult to achieve a satisfactory forecasting accuracy. In this paper, a hybrid model, Wavelet Denoising-Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EWKM), which combines k-Nearest Neighbor (KNN) and Extreme Learning Machine (ELM) based on a wavelet denoising technique is proposed for short-term load forecasting. The proposed hybrid model decomposes the time series into a low frequency-associated main signal and some detailed signals associated with high frequencies at first, then uses KNN to determine the independent and dependent variables from the low-frequency signal. Finally, the ELM is used to get the non-linear relationship between these variables to get the final prediction result for the electric load. Compared with three other models, Extreme Learning Machine optimized by k-Nearest Neighbor Regression (EKM), Wavelet Denoising-Extreme Learning Machine (WKM) and Wavelet Denoising-Back Propagation Neural Network optimized by k-Nearest Neighbor Regression (WNNM), the model proposed in this paper can improve the accuracy efficiently. New South Wales is the economic powerhouse of Australia, so we use the proposed model to predict electric demand for that region. The accurate prediction has a significant meaning. |
Keywords | electric load; predict; ELM; KNN regression; wavelet denoising |
Year | 2017 |
Journal | Energies |
Journal citation | 10 (5), p. Article 694 |
Publisher | Multidisciplinary Digital Publishing Institute (MDPI AG) |
ISSN | 1996-1073 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/en10050694 |
Scopus EID | 2-s2.0-85024400504 |
Open access | Published as ‘gold’ (paid) open access |
Page range | 1-16 |
Funder | National Natural Science Foundation of China (NSFC) |
Publisher's version | License File Access Level Open |
Output status | Published |
Publication dates | |
Online | 16 May 2017 |
Publication process dates | |
Accepted | 09 May 2017 |
Deposited | 07 Jul 2023 |
Grant ID | 41571016 |
https://acuresearchbank.acu.edu.au/item/8z3zw/a-novel-hybrid-model-based-on-extreme-learning-machine-k-nearest-neighbor-regression-and-wavelet-denoising-applied-to-short-term-electric-load-forecasting
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Publisher's version
OA_Li_2017_A_novel_hybrid_model_based_on.pdf | |
License: CC BY 4.0 | |
File access level: Open |
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