[
1. Slovak Ministry of Economy. Integrated National Energy and Climate Plan for 2021 to 2030. [Internet] 2019 December [cited 2022 May 31] Available from: https://energy.ec.europa.eu/system/files/202003/sk_final_necp_main_en_0.pdf
]Search in Google Scholar
[
2. Brożyna J, Strielkowski W, Fomina A, Nikitina N. Renewable Energy and EU 2020 Target for Energy Efficiency in the Czech Republic and Slovakia. Energies. 2020; 13(4): 965.10.3390/en13040965
]Search in Google Scholar
[
3. OKTE, a.s. National Energy Mix. [Internet] 2022 May [cited 2022 May 31] Available from: https://www.okte.sk/en/guarantees-of-origin/statistics/national-energy-mix/
]Search in Google Scholar
[
4. IEA. Gas 2020. [Internet] 2020 June [cited 2022 May 31]. Available from: https://iea.blob.core.windows.net/assets/555b268e-5dff-4471-ac1d-9d6bfc71a9dd/Gas_2020.pdf
]Search in Google Scholar
[
5. Jandačka J, Holubčík M, Trnka J. Utilization of solid fuels with regard to the transport distances of the raw material. TRANSCOM 2021, Transp Res Proc 2021; 55: 829-836.10.1016/j.trpro.2021.07.051
]Search in Google Scholar
[
6. Nandimandalam H, Gude VG, Marufuzzaman M. Enviromental impact assessment of biomass supported electricity generation for sustainable rural energy systems – A case study of Grenada County, Mississippi, USA. Sci Total Environ. 2022; 802: 149713.10.1016/j.scitotenv.2021.14971634455272
]Search in Google Scholar
[
7. Dritsaki C, Niklis D, Stamatiou P. Oil Consumption Forecasting using ARIMA Models: An Empirical Study for Greece. Int J Energy Econ Policy. 2021; 11(4):214-224.10.32479/ijeep.11231
]Search in Google Scholar
[
8. Ozturk, S, Ozturk F. Forecasting Energy Consumption of Turkey by ARIMA Model. J Asian Sci Res. 2018; 8(2): 52-60.10.18488/journal.2.2018.82.52.60
]Search in Google Scholar
[
9. Akpinar M, Yumusak N. Year ahead demand forecast of city natural gas using seasonal time series methods. Energies. 2016; 9: 727.10.3390/en9090727
]Search in Google Scholar
[
10. Chaturvedi S, Rajasekar E, Natarajan S, McCullen N. A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India. Energy Policy. 2022; 168: 113097.10.1016/j.enpol.2022.113097
]Search in Google Scholar
[
11. Manowska A, Rybak A, Dylong A, Pielot J. Forecasting of Natural Gas Consumption in Poland Based on ARIMA-LSTM Hybrid Model. Energies. 2021; 14(24): 8597.10.3390/en14248597
]Search in Google Scholar
[
12. Wang X. Research on the prediction of per capita coal consumption based on the ARIMA-BP combined model. Energy Rep. 2022: 8(4): 285-294.10.1016/j.egyr.2022.01.131
]Search in Google Scholar
[
13. Wang Q, Li S, Li R. Forecasting energy demand in China and India: Using single-linear, hybrid-linear, and non-linear time series forecast techniques. Energy. 2018; 161: 821-831.10.1016/j.energy.2018.07.168
]Search in Google Scholar
[
14. Ma M, Wang Z. Prediction of the energy consumption variation trend in South Africa based on ARIMA, NGM and NGM-ARIMA models. Energies. 2020; 13(1): 10.10.3390/en13010010
]Search in Google Scholar
[
15. Pavlicko M, Vojteková M, Blažeková O. Forecasting of electrical energy consumption in Slovakia. Mathematics. 2022; 10: 577.10.3390/math10040577
]Search in Google Scholar
[
16. Brabec M, Konár O, Pelikán E, Malý M. A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers. Int J Forecast. 2008; 24: 659-678.10.1016/j.ijforecast.2008.08.005
]Search in Google Scholar
[
17. Hošovský A, Piteľ J, Adámek M, Mižáková J, Židek K. Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA/SARMA and genetic-algorithm-optimized regression wavelet neural network models. J Build Eng. 2021; 34: 101955.10.1016/j.jobe.2020.101955
]Search in Google Scholar
[
18. BP p.l.c. Statistical Review of World Energy – all data, 1965-2020. [Internet] 2021 July [cited 2022 May 29]. Available from: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/xlsx/energy-economics/statistical-review/bp-stats-review-2021-all-data.xlsx
]Search in Google Scholar
[
19. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y. Testing the null hypothesis of stationarity against the alternative of unit root: How sure are we that economic time series have a unit root? J Econom. 1992; 54(1-3): 159-178.10.1016/0304-4076(92)90104-Y
]Search in Google Scholar
[
20. Cipra T. Time Series in Economics and Finance. Cham: Springer Nature Switzerland; 2020. 410p.10.1007/978-3-030-46347-2
]Search in Google Scholar
[
21. Hyndman RJ, Athanasopoulos G. Forecasting: Principles and Practice. 2nd ed. Heathmont: OTexts; 2018. 382p.
]Search in Google Scholar
[
22. Slovak Ministry of Economy. Report on the results of gas supply security monitoring. [Internet] 2021 July [cited 2022 October 21] Available from: https://www.mhsr.sk/uploads/files/WdL723Kw.pdf?csrt=6552196416131516580
]Search in Google Scholar
[
23. Wang Q, Li S, Jiang F. Uncovering the impact of the COVID-19 pandemic on energy consumption: New insight from difference between pandemic-free scenario and actual electricity consumption in China. J Clean Prod. 2021; 313: 127897.10.1016/j.jclepro.2021.127897975919936568686
]Search in Google Scholar
[
24. Wang Q, Li S, Zhang M, Li R. Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach. Energy. 2022; 239: 122280.10.1016/j.energy.2021.122280975971036569119
]Search in Google Scholar