Document Type: Research Paper
Tehran Area Operation Center,Tehran Regional Electric Company,Tehran, Iran
MECO ,MAPNA Company ,Karaj, Iran
Department of Electrical and Computer Engineering, WTIAU, Karaj, Iran
Department of Energy Management and Optimization, Graduate University of Advanced Technology,Kerman,Iran
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 Day-ahead 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 Ontario electricity market and compared with some of the most recently published price forecast methods.