Forecasting the electricity price in iran power market: A comparison between neural networks and time series methods

A comparative analysis of neural networks and statistical time-series methods in the electricity price forecasting of the Iran power market is conducted. An error back propagation algorithm is applied to train neural networks. The time series methods discussed in previous research were considered as benchmarks in the comparison. Two case studies are investigated based on the proposed methods to examine the impact of load ramp in the electric energy price. Only the timeseries of energy price is considered as inputs in the first case and both of the price and load are considered as inputs in the second case. The computational results validate the capability of neural networks in energy price forecasting and indicate that the load ramp is one major factor for determining the energy price.