Applications of improved grey prediction model for power demand forecasting

Hsu, Che-Chiang; Chen, Chia-Yon

doi:10.1016/s0196-8904(02)00248-0

Cited by 349 publications

(167 citation statements)

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“…The GM (1,1) has been used as a prediction tool in several different areas (Sum, 1991;Zhu and Cao, 1998;Wang, 2002;Mao and Chirwa, 2006;Chiang and Chen et al, 2008); Tang and Yin, 2012). Energy demand forecasting is another important usage area of grey prediction models (Yao et al, 2003;Zhou et al, 2006;Akay and Atak, 2007;Hsu and Chen, 2012;Chena and Wang, 2012;Lü and Lu, 2012;Chang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

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“…The model is simple to calculate and has obvious advantages for the forecasting of small sample data with irregular distribution [11,12]. The specific mathematical model of GM(1,1) is as follows:…”

Section: Gm(11)mentioning

confidence: 99%

Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

2018

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Presently, China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally. China's CO 2 emission reduction has a direct impact on global trends. Therefore, accurate forecasting of CO 2 emissions is crucial to China's emission reduction policy formulating and global action on climate change. In order to forecast the CO 2 emissions in China accurately, considering population, the CO 2 emission forecasting model using GM(1,1) (Grey Model) and least squares support vector machine (LSSVM) optimized by the modified shuffled frog leaping algorithm (MSFLA) (MSFLA-LSSVM) is put forward in this paper. First of all, considering population, per capita GDP, urbanization rate, industrial structure, energy consumption structure, energy intensity, total coal consumption, carbon emission intensity, total imports and exports and other influencing factors of CO 2 emissions, the main driving factors are screened according to the sorting of grey correlation degrees to realize feature dimension reduction. Then, the GM(1,1) model is used to forecast the main influencing factors of CO 2 emissions. Finally, taking the forecasting value of the CO 2 emissions influencing factors as the model input, the MSFLA-LSSVM model is adopted to forecast the CO 2 emissions in China from 2018 to 2025.

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“…In recent years, several methods have been proposed to model and forecast product demand, such as Bayesian Model (Lee, Lee & Lee, 2012;Pezzulli et al, 2006), Neural Network (Tilokavichai & Sophatsathit, 2011;Lau, Ho & Zhao, 2013;Singhal & Swarup, 2011;Yu & Huarng, 2010), Times Series Analysis (Box, Jenkins & Reinsel, 2013;Hunt & Amarawickrama, 2008;Zhang, Huang & Zhao, 2013), Grey Theory (Yao, Chi & Chen, 2003;Hsu & Chen, 2003) Genetic Algorithm (Haldenbilen & Ceylan, 2005) and so on. Exponential Smoothing Method (ESM) is often considered to apply for building forecasting model, which is developed by Robert G. Brown (Brown & Meyer, 1961).…”

Section: Research Framework and Purposesmentioning

confidence: 99%