1. A method of identifying electric load types of a plurality of different electric loads, said method comprising: providing a self-organizing map load feature database of a plurality of different electric load types and a plurality of neurons, each of said different electric load types corresponding to a number of said neurons; employing a weight vector for each of said neurons; sensing a voltage signal and a current signal for each of said different electric loads; determining a load feature vector comprising at least four different load features from said sensed voltage signal and said sensed current signal for a corresponding one of said different electric loads; identifying by a processor one of said different electric load types by relating the load feature vector to the neurons of said self-organizing map load feature database by identifying the weight vector of one of said neurons corresponding to said one of said different electric load types that is a minimal distance to the load feature vector; employing with said identifying the weight vector an average squared Euclidean distance to a plurality of neurons in a class corresponding to said one of said plurality of different electric load types; employing i as an index; employing ω i as said class; employing x as said load feature vector; for each of said plurality of different electric load types, employing a group of values of said self-organizing map load feature database having a mean y i and a square covariance matrix Σ i ; employing with said identifying the weight vector a point-to-cluster function of average squared Euclidean distance from said load feature vector to every point in said class, ω i , corresponding to said one of said plurality of different electric load types; employing Tr( ) as a trace of the square covariance matrix Σ i ; and determining the average squared Euclidean distance from: 
 d E ( x,ω i ) 2 =( x−y i ) T ( x−y i )+ Tr (Σ i ).