| Abstract [eng] |
The final project analyses and forecasts Lithuanian electricity prices for the period from January 1, 2024, to February 28, 2025. This topic is relevant due to the high level of public, business, and political interest in electricity price dynamics, as well as the need to plan activities and costs effectively and to ensure energy efficiency. The following variables have been identified as influencing Lithuanian electricity prices: historical electricity prices in the Swedish SE4 price zone, Poland, and Lithuania; day-ahead electricity consumption forecasts for Lithuania, the Swedish SE4 price zone, Latvia, and Poland; day-ahead electricity generation forecasts for Lithuania, Swedish SE1, SE2, SE3, and SE4 price zones, Finland, Danish DK1 and DK2 price zones, Latvia, Estonia, and Poland; forecasted solar and wind power generation in Lithuania; and commercial inter-system flows between the Swedish SE4 price area and Lithuania, between Latvia and Lithuania, and between Poland and Lithuania, as well as the gas price in Lithuania. Four univariate and four multivariate forecasting models were developed: Random Forest, Support Vector Regression, XGBoost, and LightGBM. The best-performing univariate forecasting model is the Random Forest, with parameters ntree = 300, mtry = 2, and nodesize = 5, achieving an average MAPE of 0.05%. The best-performing multivariate prediction model is XGBoost, with parameters eta = 0.05, max_depth = 6, subsample = 0.8, and colsample_bytree = 0.8. However, the multivariate prediction model did not outperform the univariate one — the XGBoost model achieved an average MAPE of 28.12%. |
