Short Term Load Forecasting Models in Czech Republic Using Soft
  Computing Paradigms
  This paper presents a comparative study of six soft computing models namely
multilayer perceptron networks, Elman recurrent neural network, radial basis
function network, Hopfield model, fuzzy inference system and hybrid fuzzy
neural network for the hourly electricity demand forecast of Czech Republic.
The soft computing models were trained and tested using the actual hourly load
data for seven years. A comparison of the proposed techniques is presented for
predicting 2 day ahead demands for electricity. Simulation results indicate
that hybrid fuzzy neural network and radial basis function networks are the
best candidates for the analysis and forecasting of electricity demand.
