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A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity

Heni Boubaker et al.

Energy Studies Review2020https://doi.org/10.15173/esr.v24i1.4135article
ABDC C
Weight
0.40

What the paper says

This study investigates the performance of a novel neural network technique in the problem of price forecasting. To improve the prediction accuracy using each model’s unique features, this research proposes a hybrid approach that combines the -factor GARMA process, empirical wavelet transform and the local linear wavelet neural network (LLWNN) methods, to form the GARMA-WLLWNN process. In order to verify the validity of the model and the algorithm, the performance of the proposed model is evaluated using data from Polish electricity markets, and it is compared with the dual generalized long memory -factor GARMA-G-GARCH model and the individual WLLWNN. The empirical results demonstrated the proposed hybrid model can achieve a better predicting performance and prove that is the most suitable electricity market forecasting technique.

5 citations

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https://doi.org/https://doi.org/10.15173/esr.v24i1.4135

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@article{heni2020,
  title        = {{A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity}},
  author       = {Heni Boubaker et al.},
  journal      = {Energy Studies Review},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.15173/esr.v24i1.4135},
}

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A New Hybrid Wavelet-Neural Network Approach for Forecasting Electricity

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Evidence weight

0.40

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.14 × 0.4 = 0.06
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.