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A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks

Author
Jahangir, H.; Tayarani, H.; Baghali, S.; Ahmadian, A.; Elkamel, A.; Golkar, M.; Castilla, M.
Type of activity
Journal article
Journal
IEEE transactions on industrial informatics
Date of publication
2020-04-01
Volume
16
Number
4
First page
2369
Last page
2381
DOI
10.1109/TII.2019.2933009
Repository
http://hdl.handle.net/2117/179325 Open in new window
https://www.researchgate.net/publication/334980997_A_Novel_Electricity_Price_Forecasting_Approach_Based_on_Dimension_Reduction_Strategy_and_Rough_Artificial_Neural_Networks Open in new window
URL
https://ieeexplore.ieee.org/document/8788555 Open in new window
Abstract
An accurate electricity price forecasting (EPF) plays a vital role in the deregulated energy markets and has a specific effect on optimal management of the power system. Considering all the potent factors in determining the electricity prices—some of which have stochastic nature—makes this a cumbersome task. In this article, first, Grey correlation analysis is applied to select the effective parameters in EPF problem and eliminate redundant factors based on low correlation grades. Then, a de...
Citation
Jahangir, H. [et al.]. A novel electricity price forecasting approach based on dimension reduction strategy and rough artificial neural networks. "IEEE transactions on industrial informatics", 1 Abril 2020, vol. 16, núm. 4, p. 2369-2381.
Keywords
Deep learning (DL), Denoising, Dimension reduction (DR), Price forecasting, Rough neuron
Group of research
PERC-UPC - Power Electronics Research Centre
SEPIC - Power and Control Electronics Systems

Participants

  • Jahangir, Hamidreza  (author)
  • Tayarani, Hanif  (author)
  • Baghali, Sina  (author)
  • Ahmadian, Ali  (author)
  • Elkamel, Ali  (author)
  • Golkar, Masoud Aliakbar  (author)
  • Castilla Fernandez, Miguel  (author)