A New Iterative Neural Based Method to Spot Price Forecasting

Subject Areas : International Journal of Smart Electrical Engineering

Mehdi KHavaninzadeh ¹ , Mohamad KHavaninzadeh ² , Mohsen KHavaninzadeh ³ , Farshid Keynia ⁴

1 - Tehran Area Operation Center,Tehran Regional Electric Company,Tehran, Iran
2 - MECO ,MAPNA Company ,Karaj, Iran
3 - Department of Electrical and Computer Engineering, WTIAU, Karaj, Iran
4 - Department of Energy Management and Optimization, Graduate University of Advanced Technology,Kerman,Iran

Received: 2016-02-05 Accepted : 2016-02-05 Published : 2015-12-01

Keywords:

Abstract :

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.