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Next-Generation Load Forecasting Architecture for Smart Energy Grids
Authors
1 2 UG Students, Department of Electrical and Electronics Engineering, J.K.K. Munirajah College of Technology, Erode, Tamil Nadu, India. 3 Professor, Department of Electrical and Electronics Engineering, J.K.K. Munirajah College of Technology, Erode, Tamil Nadu, India.
Article Information
- DOI
- No Doi
- Published In
- Year 2024 · Volume 1 · Issue 3
- Published Online
- September-December 2024
- Pages
- 12-14
- Article Type
- Research Article
- Check for updates
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References
1. Qiu, X.; Ren, Y.; Suganthan, P.N.; Amaratunga, G.A.J. Empirical mode decomposition-based ensemble deep learning for load demand time
series forecasting. Appl. Soft. Comput. 2017, 54, 246–255.
2. Qiu, X.; Suganthan, P.N.; Amaratunga, G.A.J. Ensemble incremental learning Random Vector Functional Link network for shortterm
electric load forecasting. Knowl.-Based Syst. 2018, 145, 182–196.
3. Chao, T.; Li, J.; Lang, C.; Kong, F.; Niu, J.; Rodrigues, J.J.P.C. An efficient deep model for day-ahead electricity load forecasting with
stacked denoising auto-encoders. J. Parallel Distrib. Comput. 2018, 117, 267–273.
4. Yuan, J.; Farnham, C.; Azuma, C.; Emura, K. Predictive artificial neural network models to forecast the seasonal hourly electricity
consumption for a University Campus. Sustain. Cities Soc. 2018, 42, 82–92.
5. Hamid, M.B.A.; Rahman, T.K.A. Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System
Learning Algorithm. In Proceedings of the 12th International Conference on Computer Modeling and Simulation (UKSim), Cambridge,
UK, 24–26 March 2010; pp. 408–413.
6. Wang, J.; Gao, J.; Wei, D. Electric load prediction based on a novel combined interval forecasting system. Appl. Energy 2022, 322, 119420.
7. Li, H.; Guo, S.; Li, C.; Sun, J. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly
optimization algorithm. Knowl.-Based Syst. 2013, 37, 378–387.
series forecasting. Appl. Soft. Comput. 2017, 54, 246–255.
2. Qiu, X.; Suganthan, P.N.; Amaratunga, G.A.J. Ensemble incremental learning Random Vector Functional Link network for shortterm
electric load forecasting. Knowl.-Based Syst. 2018, 145, 182–196.
3. Chao, T.; Li, J.; Lang, C.; Kong, F.; Niu, J.; Rodrigues, J.J.P.C. An efficient deep model for day-ahead electricity load forecasting with
stacked denoising auto-encoders. J. Parallel Distrib. Comput. 2018, 117, 267–273.
4. Yuan, J.; Farnham, C.; Azuma, C.; Emura, K. Predictive artificial neural network models to forecast the seasonal hourly electricity
consumption for a University Campus. Sustain. Cities Soc. 2018, 42, 82–92.
5. Hamid, M.B.A.; Rahman, T.K.A. Short Term Load Forecasting Using an Artificial Neural Network Trained by Artificial Immune System
Learning Algorithm. In Proceedings of the 12th International Conference on Computer Modeling and Simulation (UKSim), Cambridge,
UK, 24–26 March 2010; pp. 408–413.
6. Wang, J.; Gao, J.; Wei, D. Electric load prediction based on a novel combined interval forecasting system. Appl. Energy 2022, 322, 119420.
7. Li, H.; Guo, S.; Li, C.; Sun, J. A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly
optimization algorithm. Knowl.-Based Syst. 2013, 37, 378–387.
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