Energy demand classification by probabilistic neural network for medical diagnosis applications

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.