Applaying Iterative Method to Neural Network to Tehran Area load Forecasting

Electricity price predictions have become a major discussion on competitive market under deregulated power system. But, the exclusive characteristics of electricity price such as non-linearity, non-stationary and time-varying volatility structure present several challenges for this task. In this paper, a new forecast strategy based on the iterative neural network is proposed for Dayahead price forecasting. For improved accuracy of prediction an intelligent two-stage feature selection is proposed here to remove the irrelevant and redundant inputs. In order to have a fast training the neural network normalization is vital, so in this paper the above technique is used. The proposed approach is examined in the Tehran electricity market and compared with some of the most recently published price forecast methods.

Applaying Iterative Method to Neural Network to Tehran Area load Forecasting Keywords:

Electricity price forecast , Artificial Neural Network , Feature selection , Normalization , short term price forecasting ,