Energy demand classification by probabilistic neural network for medical diagnosis applications
- 18 Downloads
Forecasting in the field of power management is essential in recent days, due to the high electrical consumption at household and medical diagnosis applications to classify the electricity usage. It is highly impossible to identify the more accurate calculations in electricity consumption due to many uncertainties. This paper helps to overcome these uncertainties into probabilities by utilizing probabilistic neural network (PNN). The most complicated, complex and non-defined problems are well tackled by PNN as it is universally accepted as the best alternative technique. The conventional way of programming is not done but it is trained on the basis of behavioral representation of the data using the previous history. Multiple applications have been benefited using this system. Generally, PNN is used to differentiate four kinds of data produced from various grids and simultaneously the data of the grid are classified. 95% of reliability and accuracy is obtained from calculations produced from PNN as per the data results. The design can be used for appropriate grid development and to classify electricity usage.
KeywordsEnergy demand Prediction Probabilistic neural network Classification
The authors thankfully acknowledge support from the administration, Kalasalingam Academy of Research And Education, Krishnankoil, India. The authors would like to thank the reviewers for their valuable time to review the paper and better enhancement in further.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- 1.Goodwin M, Yazidi A (2014) A pattern recognition approach for peak prediction of electrical consumption. In Iliadis L, Maglogiannis I, Papadopoulos H (eds) Proceedings of the artificial intelligence applications and innovations AIAI 2014, IFIP advances in information and communication technology. Springer, Berlin, vol 436. http://dx.doi.org/10.1007/978-3-662-44654-6_26
- 3.Chiodo E, Lauria D (2012) Probabilistic description and prediction of electric peak power demand. In: Electrical systems for aircraft, railway and ship propulsion (ESARS) IEEE, pp 1–7. http://dx.doi.org/10.1109/ESARS.2012.6387418
- 9.Vossen J, Feron B, Monti A (2018) Probabilistic forecasting of household electrical load using artificial neural networks. In: Conference paper, June 2018. https://doi.org/10.1109/pmaps.2018.8440559
- 15.Bishop C (2000) Neural networks for pattern recognition, United States. Oxford University Press Inc, OxfordGoogle Scholar
- 16.Adeli H, Karim A (2000) Fuzzy-wavelet RBFNN model for freeway incident detection. J Transp Eng 126(6):464–471. https://doi.org/10.1061/(asce)0733-947X(2000)126:6(464) CrossRefGoogle Scholar