| Abstract [eng] |
58 pages, 4 charts, 15 pictures, 57 references. The main purpose of this master thesis is to overview and classify electricity price forecasting methods and to determine the method which is the most suitable to perform electricity prices forecast for Lithuania’s market price using Nord Pool market data of 2016–2020 October. The need of extensive methodology for electricity price forecasting rises from the energy producers to make a more personalized and informed decisions on the potential investments in the energy opportunities. It was determined that time series – autoregressive integrated moving average (ARIMA) model would be used to forecast electricity prices. After electricity prices forecast calculation was performed and reliability of the time series model was estimated. Furthermore, the forecasted prices were compared to the levelized cost of energy of various energy production technologies. The literature analysis showed that the market price is primary investment signal for the investors. The generators behaviour depends on the market price and possibility to recover of the initial capital expenditures and other fixed costs. Therefore, the comparison of the forecasted prices and levelized cost of energy was performed, which showed that the generators behaviour would result in investment in the renewable energy sources (wind and solar) generating assets. This conclusion in in line with European Green Deal road map to encourage efficient long-term investments for the energy transition to green energy. The work consists of three main parts: the analysis of literature, the creation of forecasting model and its results, conclusion, and recommendations. |
