Open Access
Issue |
E3S Web of Conf.
Volume 469, 2023
The International Conference on Energy and Green Computing (ICEGC’2023)
|
|
---|---|---|
Article Number | 00063 | |
Number of page(s) | 13 | |
DOI | https://doi.org/10.1051/e3sconf/202346900063 | |
Published online | 20 December 2023 |
- “Energies | Free Full-Text | Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review.” Accessed: Oct. 06, 2023. [Online]. Available: https://www.mdpi.com/1996-1073/16/3/1404 [Google Scholar]
- C. Tarmanini, N. Sarma, C. Gezegin, and O. Ozgonenel, “Short term load forecasting based on ARIMA and ANN approaches,” Energy Rep., vol. 9, pp. 550–557, May 2023, doi: 10.1016/j.egyr.2023.01.060. [CrossRef] [Google Scholar]
- P. Ma, S. Cui, M. Chen, S. Zhou, and K. Wang, “Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System,” Energies, vol. 16, no. 15, Art. no. 15, Jan. 2023, doi: 10.3390/en16155809. [PubMed] [Google Scholar]
- S. Shrivastava, D. K. T. Chaturvedi, and D. Scholar, “A Review of Artificial Intelligence Techniques for Short Term Electric Load Forecasting,” vol. 7, no. 5. [Google Scholar]
- E. A. Feilat and M. Bouzguenda, “Medium-term load forecasting using neural network approach,” in 2011 IEEE PES Conference on Innovative Smart Grid Technologies-Middle East, IEEE, 2011, pp. 1–5. [Google Scholar]
- L. C. P. Velasco, D. L. L. Polestico, G. P. O. Macasieb, M. B. V. Reyes, and F. B. Vasquez Jr, “Load forecasting using autoregressive integrated moving average and artificial neural network,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 7, 2018. [Google Scholar]
- D. Ali, M. Yohanna, M. I. Puwu, and B. M. Garkida, “Long-term load forecast modelling using a fuzzy logic approach,” Pac. Sci. Rev. Nat. Sci. Eng., vol. 18, no. 2, pp. 123–127, 2016. [Google Scholar]
- R. M. Holmukhe, M. S. Dhumale, M. P. Chaudhari, and M. P. Kulkarni, “Short Term Load Forecasting with Fuzzy Logic Systems for power system planning and reliability-A Review,” in AIP Conference Proceedings, American Institute of Physics, 2010, pp. 445–458. [Google Scholar]
- H. H. Çevik and M. Çunkaş, “Short-term load forecasting using fuzzy logic and ANFIS,” Neural Comput. Appl., vol. 26, pp. 1355–1367, 2015. [CrossRef] [Google Scholar]
- F. M. Butt et al., “Artificial Intelligence based accurately load forecasting system to forecast short and medium-term load demands,” Math. Biosci. Eng., vol. 18, no. 1, Art. no. mbe-18-01-022, 2021, doi: 10.3934/mbe.2021022. [Google Scholar]
- X. Pan and B. Lee, “A comparison of support vector machines and artificial neural networks for mid-term load forecasting,” in 2012 IEEE International conference on industrial technology, IEEE, 2012, pp. 95–101. [Google Scholar]
- R. Swaroop and H. A. A. Abdulqader, “Load forecasting for power system planning using fuzzy-neural networks,” in Proceedings of the world congress on engineering and computer science, San Francisco, USA, 2012, pp. 24–26. [Google Scholar]
- D. C. Nomades, “Historique Météo de Taroudant,” Historique Météo. Accessed: Aug. 28, 2023. [Online]. Available: https://www.historique-meteo.net/afrique/maroc/taroudant/ [Google Scholar]
- M. Hellmann, “Fuzzy logic introduction,” Univ. Rennes, vol. 1, no. 1, 2001. [Google Scholar]
- A. Naresh Kumar, C. Sanjay, and M. Chakravarthy, “Fuzzy inference system-based solution to locate the cross-country faults in parallel transmission line,” Int. J. Electr. Eng. Educ., vol. 58, no. 1, pp. 83–96, 2021. [CrossRef] [Google Scholar]
- C. Wang, A study of membership functions on mamdani-type fuzzy inference system for industrial decision-making. Lehigh University, 2015. [Google Scholar]
- J.-S. R. Jang and N. Gulley, “Fuzzy logic toolbox user’s guide,” Mathworks Inc, vol. 1, no. 995, p. 19, 1995. [Google Scholar]
- A. Sadollah, Fuzzy logic based in optimization methods and control systems and its applications. BoD–Books on Demand, 2018. [Google Scholar]
- M. Ahmadian, “ACTIVE CONTROL OF VEHICLE VIBRATION,” in Encyclopedia of Vibration, S. Braun, Ed., Oxford: Elsevier, 2001, pp. 37–45. doi: 10.1006/rwvb.2001.0193. [CrossRef] [Google Scholar]
- J. Lukács, “Comparison of defuzzification methods for cabin noise prediction of passenger cars,” in 2019 IEEE 17th International Symposium on Intelligent Systems and Informatics (SISY), IEEE, 2019, pp. 000115–000120. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.