Procedure for Forecasting of Electrical Non-conventional Electrical Power

Authors

  • Vijaya Krishna R. Assistant Professor, Department of EEE, GMRIT, Rajam, India Author
  • Ravi Kumar J. ssistant Professor, Department of EEE, GMRIT, Rajam, India. Author
  • Rama Krishna NSS. Assistant Professor, Department of EEE, GMRIT, Rajam, India. Author

DOI:

https://doi.org/10.61841/t1451d89

Keywords:

Procedure for Forecasting, Electrical Non-conventional, Utilization in Load Forecasting

Abstract

Load forecasting of renewable energy plants could be a terribly active analysis field, as reliable data concerning the long run are found. Forecasting helps arrange for future generation facilities and transmission augmentation. It includes historical information and present information and predicts the futuristic value. An Artificial Neural Network (ANN) approach is given for star load forecasting. The check set is used just for prediction to check the performance of the model on out-of-sample data. I actually have targeted various techniques and therefore the models out there in forecasting such as extrapolation, Correlation, and rule through extreme learning machines using an artificial neural network. Their input values, hidden neurons, weight, bias, Autoencoder utilization in load forecasting. In this review, a summary of load forecasting and their techniques are given. 

Downloads

Download data is not yet available.

References

[1] Extreme Learning Machine for Regression and Multiclass Classification Guang-Bin Huang, Senior

Member, IEEE, Hongming Zhou, Xiaojian Ding, and Rui Zhang.

[2] A comparative study between Empirical Wavelet Transforms and Empirical Mode Decomposition

Methods: Application to bearing defect diagnosis M. Kedadouche, M. Thomas n, A.Tahan

[3] R.S. Sutton, A.G. Barto, Reinforcement Learning: An Introduction, Vol. 1, MIT press Cambridge, 1998.

[4] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller, Playing atari with deep reinforcement learning, arXiv Preprint arXiv:1312.5602.

[5] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J.Schrittwieser, I.

Antonoglou, V. Panneershelvam, M. Lanctot, et al., Mastering the game of Go with deep neural networks and tree search, Nature 529 (7587)(2016) 484–489.

[6] C. Reiss, J. Wilkes, J.L. Hellerstein, Google cluster-usage traces: format + schema, [Online]. Available: http://code.google.com/p/googleclusterdata/wiki/TraceVersion2, Nov. 2011.

[7] V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G. Bellemare, A.Graves, M. Riedmiller,

A.K. Fidjeland, G. Ostrovski, et al., Human-level control through deep reinforcement learning, Nature

518(7540) (2015) 529–533.

[8] T.P. Lillicrap, J.J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, D. Wierstra,Continuous control with deep reinforcement learning, arXiv Preprint arXiv:1509.02971.

[9] V. Mnih, A.P. Badia, M. Mirza, A. Graves, T.P. Lillicrap, T. Harley, D. Silver, K. Kavukcuoglu,

Asynchronous methods for deep reinforcement learning, in: International Conference on Machine

Learning, 2016.

[10] H. Van Hasselt, A. Guez, D. Silver, Deep reinforcement learning with double q-learning, in: AAAI, 2016, pp. 2094–2100.

Downloads

Published

29.02.2020

How to Cite

R. , V. K., J. , R. K., & NSS. , R. K. (2020). Procedure for Forecasting of Electrical Non-conventional Electrical Power. International Journal of Psychosocial Rehabilitation, 24(1), 1642-1651. https://doi.org/10.61841/t1451d89