Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China

Zhou, Weijie; Wu, Xudong; Ding, Song; Pan, Jiao

doi:10.1016/j.energy.2020.117443

Cited by 60 publications

(25 citation statements)

References 28 publications

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“…We observe from equations ( 3) and ( 9) that the discretization method, so-called trapezoid formula, is employed to approximately calculate the integral x (1) dt, thus producing the discretization error in such a transition process. To be specific, when the above integral has a concave trend, the approximate value is larger than the actual one; when the above integral has an upward convex trend, the approximate value is lower than the actual one.…”

Section: Error Analysismentioning

confidence: 99%

“…Accordingly, there exist a plenty of approaches for time-series analysis and forecasting. From [1,2] and the references therein, these methods can be divided into three categories: statistical methods (e.g., regression analysis [3], functional state space model [4], logistic regression [5], spatial-temporal model [6], Markov chain model [7], etc. ), machine learning methods [8][9][10][11], and grey modeling technique [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Step 1. Suppose that the original time series is (1) (k n )) denotes the first-order accumulated generating operation sequence of X (0) , where…”

Section: Introductionmentioning

confidence: 99%

“…where z (1) (k i ) is the background value and z (1) (k i ) � 0.5(x (1) (k i ) + x (1) (k i− 1 )), k � 2, 3, . .…”

Section: Introductionmentioning

confidence: 99%

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An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Liu

2021

Complexity

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Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

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Section: Error Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Liu

2021

Complexity

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“…As previous literature revealed, for the ability to analyse and process data sequences, grey-based models have been broadly applied among diverse disciplines because of their excellent implementation on small-scale sample modeling, such as natural gas consumption [2][3][4], electric power supply and demand [5][6][7][8], renewable energy [9,10], industry [11,12], and medicine [13,14]. Although grey-based models have their own advantages, there still exist some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

An Improved Nonhomogeneous Grey Model with Fractional-Order Accumulation and Its Application

Liu

2021

Journal of Mathematics

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The nonhomogeneous grey model has been seen as an effective method for forecasting time series with approximate nonhomogeneous index law, which has been widely used in diverse disciplines on account of its high prediction precision. However, there remains room for improvements. For this, this study presents an improved nonhomogeneous grey model by incorporating the dynamic integral mean value theorem and fractional accumulation simultaneously. In order to promote the efficacy of the optimised model, we apply the whale optimization algorithm (WOA) to ascertain its optimal parameter. In particular, two examples are conducted to validate the superiority of the proposed model in contrast with other benchmarks, and the experimental results show that the mean absolute percentage error of the proposed approach is 808692% and 6.0706%, respectively, indicating the proposed approach performs better than other competing models.

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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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Application of a novel discrete grey model for forecasting natural gas consumption: A case study of Jiangsu Province in China

Cited by 60 publications

References 28 publications

An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

An Improved Nonhomogeneous Grey Model with Fractional-Order Accumulation and Its Application

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

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