Autor(s):

Ekaterina Popovska, Institute of Robotics, Bulgarian Academy of Sciences, ekaterina.popovska@gmail.com

Galya Georgieva-Tsaneva, Institute of Robotics, Bulgarian Academy of Sciences, galitsaneva@abv.bg

https://doi.org/10.55630/STEM.2022.0418

Abstract:

    Electricity price forecasting becomes a significant challenge on a day-to-day basis and price variations are even more volatile on an hourly basis. Therefore, this paper is used several approaches to analyze the Bulgarian hourly electricity price dynamics in the day-ahead market. Proper analysis crucially depends on the choice of an adequate model. Reviewed are the factors which may influence the electricity spot prices and characteristics of the time series of prices. Methods include and variety of modeling approaches that are applied and evaluated for forecasting electricity prices such as time-series models and regression models. The forecasting technique is to model day-ahead spot prices using well known ARIMA/SARIMA model including stationarity checks, seasonal decompose, differencing, autoregressive modeling, and autocorrelation to analyze and forecast time series hourly data. For each approach, model estimates and forecasts are developed using hourly price data, reshaped, and aggregated data on a daily and monthly basis for the Bulgarian day-ahead market.

Keywords:

Electricity Price Forecasting; Day-Ahead Market; Time Series Forecasting; Machine Learning; ARIMA; SARIMA; Modeling Approaches;

Cite (APA style):

Popovska, E., Georgieva-Tsaneva, G. (2022). ARIMA Model for Day-Ahead Electricity Market Price Forecasting, Science Series "Innovative STEM Education", volume 04, ISSN: 2683-1333, Institute of Mathematics and Informatics – Bulgarian Academy of Sciences, pp. 149-161, DOI: https://doi.org/10.55630/STEM.2022.0418

PDF file address:

http://www.math.bas.bg/vt/stemedu/book-4/STEMedu-2022-xviii.pdf