Dynamic grey platform for efficient forecasting management

The aim of the paper is to develop a grey model for short term forecasting of natural gas consumption and production in China and USA respectively. To enhance its prediction accuracy, the outliers are found by the error of the latent information function, and then corrected according to the test sample and the future trend. The sequence with corrected outliers is used to construct a grey model. The proposed model is employed to predict the natural gas consumption and production in China and USA. The results have demonstrated that the proposed model can raise the forecast accuracy of the grey model, and it also indicates that China will inevitably face a massive expansion in natural gas imports.

show abstract

“…The benchmarking data set is from reference [32]. These models are estimated for tourism demand during the period of 1989-2000.…”

Section: Casementioning

confidence: 99%

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Zhang

Zhao

2019

Scientia Iranica

View full text Add to dashboard Cite

show abstract

“…Initialize particles: the particle size in a certain area affects the particle in the search for an optimal solution and iteration speed. The particle sizes of 25 to 300 18 are generally adopted, with 50 being set as standard in this study. Furthermore, the max particle speed is set as 1, the particle position is ranged between 0 and 1, the max iteration number is set as 2000, c 1 = c 2 = 2 , ω max = 0 . 9 , and ω max = 0 . 4 .…”

Section: Componentsmentioning

confidence: 99%

Forecast of short-term wind power based on a novel hybrid method

Huang

Ren

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

Short-term wind power forecasting plays an important role in power generation, because it prevents the power system operation from its uncertain and intermittent nature. This article proposes a novel method for short-term wind power forecasting, which combines the wavelet transform, particle swarm optimization dynamic gray model and Lyapunov exponent prediction method. First, the approach decomposes the wind power curve into the high-frequency and low-frequency curves by wavelet transform, which represent the detail and tendency signals, respectively. Then, we use the proposed particle swarm optimization dynamic gray model to forecast the low-frequency curve with its smooth and periodic outline. Moreover, Lyapunov exponent prediction method is used to predict high-frequency curves, which possess the chaos characteristics. Finally, we obtain the wind power forecasting result from the combination of the predicted low and high frequencies. The experiment of four seasons in an US wind farm validates that the proposed method is effective in solving the short-term wind power forecasting problem. The obtained results, discussed comprehensively, show that the hybrid method has better prediction accuracy than the other methods, such as artificial neural network, persistence, and autoregressive integrated moving average model, with the lowest average mean absolute percentage error is 8.07% and the average root mean square error is 0.8164 over four seasons.

show abstract

“…An expression introducing of rolling modeling data and data of forecast results show their average residual error different from rolling modeling GM (1,1).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a longitudinal study by Deng [3,4,5,6] reported that the first systematic study of Grey theory in 1982, which has been recognized and applied by many academicians in different subjects such as economic prediction [20,24,25,29,31], material science [11,12,14,15], electrical power [2,8,9,10], traffic [7,17,21,26], technological progress [1,13,16], engineering [18,19] and agriculture [22], chosen Grey prediction as an ability forecasting means because of having relatively low data requirements, and a GM model constructed from a sample of just four pieces of data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Grey Forecasting Method of Total Domestic Coffee Consumption in Indonesia

Wang¹,

Ghalih²

2017

IJBER

View full text Add to dashboard Cite

Abstract:Indonesia is the one of the world's top coffee producing and exporting countries. Meanwhile, in this study, only focus on forecasting the total of domestic coffee consumption in Indonesia applied the Grey differential model which is called GM (1,1) model of Grey theory to predict the amount of domestic coffee consumption in Indonesia from 1990 to 2017. According to the estimated result, the average residual error of the Grey forecast model is over 5 percent. The model predicts that the total of consumption will increase in each year. Based on the experimental results, this proposed method apparently not only improve the forecasting accuracy of the original Grey models but also provide a valuable reference for Indonesia coffee farmer and industries to make the action plan for the future.

show abstract

Dynamic grey platform for efficient forecasting management

Cited by 13 publications

References 36 publications

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Forecasting natural gas production and consumption using grey model with latent information function: The cases of China and USA

Forecast of short-term wind power based on a novel hybrid method

Evaluation of Grey Forecasting Method of Total Domestic Coffee Consumption in Indonesia

Contact Info

Product

Resources

About