Next Article in Journal
Inverse Open Circuit Voltage Curve Model for LiCoO2 Battery at Different Temperatures
Previous Article in Journal
Profitability of Energy Sector Companies in Poland: Do Internal Factors Matter?
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Correction

Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322

by
Saravanakumar Venkatesan
* and
Yongyun Cho
Department of Information and Communications Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea
*
Author to whom correspondence should be addressed.
Energies 2024, 17(20), 5136; https://doi.org/10.3390/en17205136
Submission received: 30 September 2024 / Accepted: 10 October 2024 / Published: 16 October 2024

Funding Update

In the original publication [1], the Funding “This work was partly supported by the Innovative Human Resource Development for Local Intellectualization program through the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (IITP-2024-2020-0-01489, 50%) and the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) (RS-2024-00259703, 50%)” should not have been included. The updated Funding part is below:
Funding: This research received no funding.

Change of Corresponding Author

The corresponding author has been changed from “Yongyun Cho” to “Saravanakumar Venkatesan”.

Acknowledgements Update

The Acknowledgements part has been deleted.
The authors state that the scientific conclusions are unaffected. This correction was approved by the Academic Editor. The original publication has also been updated.

Reference

  1. Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Venkatesan, S.; Cho, Y. Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322. Energies 2024, 17, 5136. https://doi.org/10.3390/en17205136

AMA Style

Venkatesan S, Cho Y. Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322. Energies. 2024; 17(20):5136. https://doi.org/10.3390/en17205136

Chicago/Turabian Style

Venkatesan, Saravanakumar, and Yongyun Cho. 2024. "Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322" Energies 17, no. 20: 5136. https://doi.org/10.3390/en17205136

APA Style

Venkatesan, S., & Cho, Y. (2024). Correction: Venkatesan, S.; Cho, Y. Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture. Energies 2024, 17, 4322. Energies, 17(20), 5136. https://doi.org/10.3390/en17205136

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop