Research of combination of electricity GM (1,1) and seasonal time series forecasting model

Wang, Yuping; Zheng, Fangpeng; He, Kelei; Li, Chunxue

doi:10.2991/icitmi-15.2015.45

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Wang et al (2012), GM(1,1), SFGM(1,1) and APL-SFGM(1,1) models for electricity demand of South Australia for the period from 2002 to 2010. Wang et al (2015), forecasted sold the electricity quantity of Guizhou Province using a GM(1,1) model for 2013 by monthly basis. Zhao et al (2016), developed GM(1,1), Rolling GM(1,1) and Rolling-FOA-GM(1,1) models for forecasting of Inner Mongolia's electricity consumption for the period from 2001 to 2014.…”

Section: Literature Reviewmentioning

confidence: 99%

Future Electricity Demand of the Emerging European Countries and the CIS Countries

Bayramoğlu¹

2016

IJFBS

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Nowadays, one of the leading factors used in the evaluation of a country’s economic development is energy consumption. Because of economic growth, demand for energy is also increasing. In this study, the emerging European countries’ (the Czech Republic, Poland, Romania, Turkey) and the CIS countries’ (Kazakhstan, Russia, Ukraine, Uzbekistan) electricity consumption has been forecasted for five years period (2015-2019). In the study, GM(1,1) Rolling Model, which is developed in the framework of Grey System Theory is used as a mathematical model for real-time forecasting. The results of the study show that there will not be a significant change in electricity demand in this two area during the 2015-2109 period.

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Section: Literature Reviewmentioning

confidence: 99%

Future Electricity Demand of the Emerging European Countries and the CIS Countries

Bayramoğlu¹

2016

IJFBS

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How to Predict Energy Consumption in BRICS Countries?

Khan

Osińska

2021

Energies

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Brazil, Russia, China, India, and the Republic of South Africa (BRICS) represent developing economies facing different energy and economic development challenges. The current study aims to predict energy consumption in BRICS at aggregate and disaggregate levels using the annual time series data set from 1992 to 2019 and to compare results obtained from a set of models. The time-series data are from the British Petroleum (BP-2019) Statistical Review of World Energy. The forecasting methodology bases on a novel Fractional-order Grey Model (FGM) with different order parameters. This study contributes to the literature by comparing the forecasting accuracy and the predictive ability of the with traditional ones, like standard and models. Moreover, it illustrates the view of BRICS’s nexus of energy consumption at aggregate and disaggregates levels using the latest available data set, which will provide a reliable and broader perspective. The Diebold-Mariano test results confirmed the equal predictive ability of for a specific range of order parameters and the model and the usefulness of both approaches for energy consumption efficient forecasting.

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Research of combination of electricity GM (1,1) and seasonal time series forecasting model

Cited by 2 publications

References 8 publications

Future Electricity Demand of the Emerging European Countries and the CIS Countries

Future Electricity Demand of the Emerging European Countries and the CIS Countries

How to Predict Energy Consumption in BRICS Countries?

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