Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Zheng, Chengli; Wu, Wei; Jiang, Jianming; Li, Qi

doi:10.1155/2020/3257328

Cited by 9 publications

(4 citation statements)

References 40 publications

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“…The grey prediction models have a good fitting ability for small sample data with poor information, and many scholars use the model for long-term NGC prediction. For example, Zheng et al (2020) predicted the NGC of China using the optimized nonhomogeneous grey model. Ding (2018) and Zeng and Li (2016) predicted China's natural gas demand by using a self-adapting intelligent grey model.…”

Section: Ngc Forecastingmentioning

confidence: 99%

Forecasting annual natural gas consumption via the application of a novel hybrid model

Gao

Shao

2021

Environ Sci Pollut Res

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Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

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Section: Ngc Forecastingmentioning

confidence: 99%

Forecasting annual natural gas consumption via the application of a novel hybrid model

Gao

Shao

2021

Environ Sci Pollut Res

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“…en, the relevant differential equation model is established. e GM prediction steps are as follows [32,33].…”

Section: Complexitymentioning

confidence: 99%

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

et al. 2020

Complexity

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In order to improve the prediction accuracy of mining safety production situation and remove the difficulty of model selection for nonstationary time series, a grey (GM) autoregressive moving average (ARMA) model based on the empirical mode decomposition (EMD) is proposed. First of all, according to the nonstationary characteristics of the mining safety accident time series, nonstationary original time series were decomposed into high- and low-frequency signals using the EMD algorithm, which represents the overall trend and random disturbances, respectively. Subsequently, the GM model was used to predict high-frequency signal sequence, while the ARMA model was used to predict low-frequency signal sequence. Finally, aiming to predict the mining safety production situation, the EMD-GM-ARMA model was constructed via superimposing the prediction results of each subsequence, thereby compared to the ARIMA model, wavelet neural network model, and PSO-SVM model. The results demonstrated that the EMD-GM-ARMA model and the PSO-SVM model hold the highest prediction accuracy in the short-term prediction, and the wavelet neural network has the lowest prediction accuracy. The PSO-SVM model’s prediction accuracy decreases in medium- and long-term predictions while the EMD-GM-ARMA model still can maintain high prediction accuracy. Moreover, the relative error fluctuations of the EMD-GM-ARMA model are relatively stable in both short-term and medium-term predictions. This shows that the EMD-GM-ARMA model can provide high-precision predictions with high stability, proving the model to be feasible and effective in predicting the mining safety production situation.

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“…Ding 33 uses a forecasting technique by combining the gray forecasting model and trigonometric residual modification for forecasting the DNLGSCN of households and finds this model can improve the prediction accuracy of the DNLGSCN. In addition to the abovementioned use of gray prediction to predict DNLGSCN, there are also some newly developed gray prediction models, such as a novel gray Bernoulli model, 35 the optimized nonhomogeneous gray model, 36 a discrete gray model, 37 a fractional time-delayed model optimized by the gray wolf optimizer, 38 the Nash nonlinear gray Bernoulli model optimized by the multiverse optimizer algorithm, 39 and a moth flame optimization algorithm-based conformable fractional nonhomogeneous gray Bernoulli model. 40 Furthermore, some researchers have tried to combine the gray forecasting model with other theories to forecast natural gas consumption.…”

Section: Introductionmentioning

confidence: 99%

Short‐term natural gas consumption prediction based on wavelet transform and bidirectional long short‐term memory optimized by Bayesian network

Liu²,

Qiao

2022

Energy Science & Engineering

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In the energy structure, natural gas is an important clean energy, and its consumption forecast is of great significance for energy policy formulation, pipeline network planning design, and peak shaving capacity determination. In the field of forecasting, wavelet transform (WT) is widely used to process time series, especially natural gas consumption. However, in the application of the WT, the wavelet's orders and levels are randomly fixed. To solve this problem, this study proposes a hybrid model, which is based on bidirectional long short‐term memory (BiLSTM). In addition, Bayesian networks are used to optimize the hyperparameters of BiLSTM. And the different wavelets' orders and levels are utilized to deal with the daily natural gas consumption (DNLGSCN). When each level of Coiflets wavelets' four and five orders are used to process the DNLGSCN, the fifth level of Coiflets wavelets' four and five orders have the highest prediction accuracy, respectively, which is 0.3318 and 0.1773, taking U1 as an example. Using Symflets wavelet to decompose the DNLGSCN also gives the same result. Compared with the decomposition method (e.g., ensemble empirical mode decomposition), the prediction performance of the above four wavelet decomposition methods is better. The main conclusions are that decomposing the DNLGSCN with the fifth level of Coiflets wavelet's five orders has the best prediction performance.

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Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Cited by 9 publications

References 40 publications

Forecasting annual natural gas consumption via the application of a novel hybrid model

Forecasting annual natural gas consumption via the application of a novel hybrid model

EMD-GM-ARMA Model for Mining Safety Production Situation Prediction

Short‐term natural gas consumption prediction based on wavelet transform and bidirectional long short‐term memory optimized by Bayesian network

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