Prediction of Hydropower Energy Price Using Gómes-Maravall Seasonal Model

The present research is aimed at investigating the possibility of predicting average monthly electricity prices and presenting a model for predicting electricity price in Iranian market considering unique characteristics of electricity as a commodity. For this purpose, time series data on average monthly electricity price during 2006–2015 was used. Firstly, unit root test was used to investigate stationarity of time series of electricity price. Then, using Gomes-Maravall model, an ARIMA model was estimated for predicating electricity price in Iranian market using energy purchase data from a hydropower plant. The model was run utilizing SEATS (Signal Extraction in ARIMA Time Series) and TARMO (“Time Series Regression with ARIMA Noise, Missing Observations, and Outliers”) programs. For this purpose, energy purchase data from three Karun river hydropower plants (Khuzestan Province, Iran) was used.