Prediction of Hydropower Energy Price Using Go’mes-Maravall Seasonal Model

Abstract

The present research is aimed at investigating the possibility of predicting average monthly 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 Go’mes-Maravall model, an ARIMA model was estimated for predicting electricity price in Iranian market using energy purchase data from a hydropower plant. The model was run utilizing SEAT (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 river hydropower plants (Khuzestan Province, Iran) was used