E3S Web Conf.
Volume 217, 2020International Scientific and Practical Conference “Environmental Risks and Safety in Mechanical Engineering” (ERSME-2020)
|Number of page(s)||6|
|Section||Digital Technologies for Sustainability|
|Published online||14 December 2020|
Modeling and forecasting seasonal and cyclical events using retrospective data
1 Volgograd State Agrarian University, 26, Universitetskiy Ave., Volgograd, 400002, Russian Federation
2 All-Russian research Institute of irrigated agriculture, 9, Timiryazeva, Volgograd, 400002, Russian Federation
* Corresponding author: firstname.lastname@example.org
Based on the data of the Ministry of energy of Russian Federation for the period of 2012-2019 years the mathematical modeling of statistics of power production were performed using time series analysis in Statistica. To describe the statistical data, the exponential smoothing model was used, its parameter was found and short-term forecasting of electricity generation in Russia for 2019 was performed. For long-term forecasting the seasonal model of autoregression and integrated moving average ARIMA - (0,1,1)(0,1,1) is chosen, its parameters are defined and its adequacy to real data is proved. With the help of the found model, the forecasting of electricity generation in the Russian Federation within 90% of the confidence interval for twelve months 2019-2020 is performed. The non-stationary time series of the studied statistics, the presence of a trend and a seasonal component in it are proved. The model parameters are defined as ARIMA(0,1,1)(0,1,1) by means of the Statistica software, and the adequacy of this model to real data is proved. Using the found mathematical model, a long – term forecast (period-12 months) of electricity generation in the Russian Federation with a 90% confidence interval was performed.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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