Retraction of: Scientific Reports https://doi.org/10.1038/s41598-021-96501-6, published online 30 August 2021
The Editors have retracted this Article.
Following publication of this Article, a number of overlaps were identified between the data presented in Tables 2, 3 and 8, and the previous literature1,2,3. In addition, concerns have been raised about the relevance of the references provided, and accuracy of the authorship contributions. The authors were unable to provide a revised version with valid referencing, and unambiguously establish the veracity of the authorship contributions. Given these concerns the Editors no longer have confidence in the study.
Rahmad Syah, Marischa Elveny, Mehdi Nesaht, and Afshin Davarpanah disagree to this retraction. Mohammad Rezaei, Meysam Majidi Nezhad, and Dadan Ramdan have not responded to any correspondence from the Editors about this retraction.
References
Anbazhagan, A. & Kumarappan, N. Day-ahead deregulated electricity market price forecasting using neural network input featured by DCT. Energy Convers. Manag. 78, 711–719. https://doi.org/10.1016/j.enconman.2013.11.031 (2014).
Elattar, E. E. Price forecasting of electricity markets based on local Gaussian process. Int. J. Eng. Innov. Res. 2(5), 453–458 (2013).
Ghasemi-Marzbali, A. A developed short-term electricity price and load forecasting method based on data processing, support vector machine, and virus colony search. Energ. Effic. 13(7), 1525–1542. https://doi.org/10.1007/s12053-020-09898-w (2020).
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Syah, R., Rezaei, M., Elveny, M. et al. Retraction Note: Day-ahead electricity price forecasting using WPT, VMI, LSSVM-based self adaptive fuzzy kernel and modified HBMO algorithm. Sci Rep 12, 2608 (2022). https://doi.org/10.1038/s41598-022-06630-9
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DOI: https://doi.org/10.1038/s41598-022-06630-9
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