Day ahead price forecasting in deregulated electricity market using Artificial Neural Network

Abstract

Now a days the price forecasting plays a very essential role in a new electricity industry; it helps the independent generators to set up optimal bidding patterns and also for designing the physical bilateral contracts. In general, different market players need to know future electricity prices as their profitability depends on them. There are many papers have been presented on the forecasting of electricity market price such methods are based on time series, artificial intelligence and hybrid methods. In this paper, the price forecasting is presented by using feed forward artificial neural network by using historical price data. Accurately and efficiently forecasting of electricity price is more important. Therefore in this paper, an Artificial Neural Network (ANN) model is designed for short term price forecasting of electricity in the environment of restructured power market. The proposed ANN model is a four-layered neural network, which consists of, input layer, two hidden layers and output layer. Matlab is used for training the proposed ANN model. Electricity load and wind forecasting can also be done using this method which helps in planning and operation of the power system.