Lithuanian electricity market price forecasting model based on univariate time series analysis

Abstract

Electricity price changes can significantly affect expenses in energy intensive industries, adjust profits or losses for electricity re-tailers and cause problems for country’s national energy strategy implementation. Forecasting models based on statistical methods and previous variable values help to predict future values and adjust strategy according to the forecast. This paper concentrates on the Lithuanian electricity market and presents the widely used ARIMA forecasting models based on the univariate time series analysis. The Lithuanian electricity market is selected due to a lack of statistical researches based on electricity market prices in Lithuania, as well as significant future electricity market liber-alization projects. Electricity price data for analysis are taken from the Nord Pool electricity market operator website. The Nord Pool represents the Northern Europe electricity market operator where Lithuania and other 14 European countries trade electricity on a daily basis. To provide a long-term electricity price outlook average monthly data from July 2012 to December 2019 are selected for analysis. Before building the ARIMA model data are tested with various statistical tests to guarantee that time series are stationary, there is no autocorrelation or structural breaks. Once the data va-lidity is confirmed, the time series is divided into train and test sets. The train data set is used to create a fitting ARIMA model, while the test set is used to define forecasting accuracy. Created forecasts of models are compared between each other using common comparison statistics, and the most accurate models are defined. Finally, the selected model is trained on a full dataset and the electricity price forecast for the year 2020 is constructed. The created AR (1) model had the smallest error value compared to the test dataset, while the SARIMA (1,1,1) model had the best approximation statistics. By combining both models the weighted SARIMA (1,1,1) model is constructed with the fea-tures of low forecasting error and precise actual time series ap-proximation. The final model forecast for the year 2020 shows the monthly average electricity price decrease at the beginning of the year, a significant increase at the second half of the year and a price drop at the end of the year. Forecasting results can help companies to plan their electricity production and maintenance periods to maximize income from sold energy and minimize po-tential losses due to planned shutdown.