The energy consumption forecasting in Mongolia based on Box-Jenkins method (Arima model)

• Autorzy: Zolboo Gansukh , Adiya Bor , Bilguun Enkhbayar
• Języki publikacji: EN

Abstrakty

EN

The primary products of the power industry are electric energy and thermal energy. Thus, forecasting electric energy consumption is significant for short and long term energy planning. ARIMA model has adopted to forecast energy consumption because of its precise prediction for energy consumption. Our result has shown that annual average electric energy consumption will be 10,628 million kWh per year during 2019-2030 which approximately 3.3 percent growth per annum. At the moment, there is not a practice solution for the storage of electricity in Mongolia. Therefore, energy supply and demand have to be balanced in real-time for operational stability. Without an accurate forecast, the end-users may experience brownouts or even blackouts or the industry could be faced with sudden accidents due to the energy demand. For this reason, energy consumption forecasting is essential to power system stability and reliability.

Słowa kluczowe

EN

energy forecasting; energy consumption; ARIMA model; Box-Jenkins method

• Wydawca: Instytut Polityki Energetycznej im. Ignacego Łukasiewicza
• Czasopismo: Energy Policy Studies
• Rocznik: 2019
• Tom: No. 1 (3)
• Strony: 70--77
• Opis fizyczny: Bibliogr. 16 poz., tab., wykr.

Twórcy

autor

Zolboo Gansukh

• School of Economics and Management, Yanshan University, PR China

autor

Adiya Bor

• East Siberia State University of Technology and Management, Mongolia

autor

Bilguun Enkhbayar

• School of Information Science and Engineering at Yanshan University

Bibliografia

• 1. Albayrak, Ali Sait. 2010. “ARIMA Forecasting of Primary Energy Production and Consumption in Turkey: 1923-2006.” Enerji, Piyasa ve Düzenleme 1:24-50.
• 2. Box, George E. P., M. Ljung Greta, Gwilym M. Jenkins, and Gregory C. Reinsel,. n.d. Time Series Analysis: Forecasting and Control, 5th Edition.
• 3. Ediger, Volkan Ş. and Sertaç Akar. 2007. “ARIMA Forecasting of Primary Energy Demand by Fuel in Turkey.” Energy Policy 35(3): 1701-8.
• 4. Etuk, Ette Harrison, Alapuye Gbolu Eleki, and Pius Sibeate. 2016. “A Box-Jenkins Model for Monthly Natural Gas Production in Nigeria.” Journal of Multidisciplinary Engineering Science Studies 2(11):6.
• 5. Hipel, Keith William, Angus Ian McLeod, and William C. Lennox. 1977. “Advances in Box-Jenkins Modeling: 1. Model Construction.” Water Resources Research 13(3):567-75.
• 6. Hor, C. L., S. J. Watson, and Shanti Majithia. 2006. “Daily Load Forecasting and Maximum Demand Estimation Using ARIMA and GARCH.” 2006 International Conference on Probabilistic Methods Applied to Power Systems 1-6.
• 7. Jiang, Feng, Xue Yang, and Shuyu Li. 2018. “Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model.” Sustainability 10(7):2225.
• 8. Lai, Sue Ling, Ming Liu, Kuo Cheng Kuo, and Ray Chang. 2014. “Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models.” Applied Mechanics and Materials.
• 9. Li, Yiyan, Dong Han, and Zheng Yan. 2018. “Long-Term System Load Forecasting Based on Data-Driven Linear Clustermg Method.” Journal of Modern Power Systems and Clean Energy 6(2):306-16.
• 10. Mohamed, Z. and P. S. Bodger. 2004. “Forecasting Electricity Consumption: A Comparison of Models for New Zealand.” in Engineering: Conference Contributions.
• 11. Morales, Juan M., Antonio J. Conejo, Henrik Madsen, Pierre Pinson, and Marco Zugno. 2014. Integrating Renewables in Electricity Markets: Operational Problems. Springer US.
• 12. National Statistical Office, n.d. “Mongolian Statistical Information Service.” Mongolian Statistical Information Service. Retrieved July 8, 2019 (http://1212.mn/).
• 13. Nichiforov, C., I. Stamatescu, I. Făgărăşan, and G. Stamatescu. 2017. “Energy Consumption Forecasting Using ARIMA and Neural Network Models.” Pp. 1-4 in 2017 5th International Symposium on Electrical and Electronics Engineering (ISEEE).
• 14. Oppenheim, Rosa. 1978. “Forecasting via the Box-Jenkins Method.” Journal of Academy of Management Science 6(No3):206-21.
• 15. Sovacool, Benjamin K., Anthony L. D’Agostino, and Malavika Jain Bambawale. 2011. “Gers Gone Wired: Lessons from the Renewable Energy and Rural Electricity Access Project (REAP) in Mongolia.” Energy for Sustainable Development 15(1):32-40.
• 16. World Bank. n.d. “World Bank Open Data | Data.” World Bank Open Data. Retrieved July 8, 2019 (https ://data.worldbank.org/).

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).