Review on Design and Simulation of Electricity Price Fore Casting Using Artificial Neural Network

Abstract

Setting the price for electricity is the main task in the reorganized power markets. Forecasting power costs well and precisely has therefore become more crucial. An ANN (Artificial Neural Network) model specifically designed for short-term forecasting of prices in restructuring electricity markets is presented in this research. The input layer, two layers that are concealed, and output layer make up the four layers of the suggested ANN model, which is a perceptron neural network. In place of traditional back propagation, the Levenberg-Marquardt back propagation (LMBP) technique is used for ANN training in order to accelerate convergence. The suggested ANN model is trained using MATLAB, and its effectiveness and performance are shown through a use in the Ontario energy market. Key Word: Fuel cell electric vehicle, high voltage gain IBC, PEMFC, MPPT, RBFN etc.