Petroleum products consumption forecasting based on a new structural auto-adaptive intelligent grey prediction model

Sapnken, Flavian Emmanuel; Tamba, Jean Gaston

doi:10.1016/j.eswa.2022.117579

Cited by 11 publications

(6 citation statements)

References 42 publications

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“…In Equations (35) to (37), n is the sample size used for the simulations, while h denotes the forecast horizon. Of all these criteria, MAPE in particular, is the reference criterion and its threshold values are given in Table 1 (Source: Sapnken and Tamba (2022, p. 8)). The smallest statistics of these criteria indicate the best model.…”

Section: Verifications Of Neuralode-gm(11) Modelmentioning

confidence: 99%

“…Given the limited number of data on the consumption of Cameroonian petroleum products, predicting it with a GM model is therefore an appropriate choice. Data on Cameroon's petroleum products consumption for the period 1996–2017 are reported in Table 2 (Source: Sapnken and Tamba (2022, p. 12)). They were collected from the Cameroon Company of Petroleum Deposits ( https://www.scdp.cm ).…”

Section: Verifications Of Neuralode-gm(11) Modelmentioning

confidence: 99%

“…Since large data are not always readily available, researchers have focused on this problem and have developed models that can be implemented with a small amount of data. Neural networks (NNs) (Gairaa et al , 2016), fuzzy logic (Hadavandi et al , 2010) and grey system theory (Sapnken and Tamba, 2022) are a few examples.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Learning latent dynamics with a grey neural ODE prediction model and its application

Sapnken

Ahmat

Boukar

et al. 2023

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PurposeIn this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.Design/methodology/approachFor this, the proposed model introduces a new image equation that is solved by the Runge-Kutta fourth order method, which makes it possible to optimize the sequence prediction function. The novel model can then capture the characteristics of the input data and completely excavate the system's evolution law through a learning procedure.FindingsThe new model has a broader applicability range as a result of this technique, as opposed to grey models, which have fixed structures and are sometimes over specified by too strong assumptions. For experimental purposes, the neural differential grey model is implemented on two real samples, namely: production of crude and consumption of Cameroonian petroleum products. For validation of the new model, results are compared with those obtained by competing models. It appears that the precisions of the new neural differential grey model for prediction of petroleum products consumption and production of Cameroonian crude are respectively 16 and 25% higher than competing models, both for simulation and validation samples.Originality/valueThis article also takes an in-depth look at the mechanics of the new model, thereby shedding light on the intrinsic differences between the new model and grey competing models.

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Section: Verifications Of Neuralode-gm(11) Modelmentioning

confidence: 99%

Section: Verifications Of Neuralode-gm(11) Modelmentioning

confidence: 99%

See 1 more Smart Citation

Learning latent dynamics with a grey neural ODE prediction model and its application

Sapnken

Ahmat

Boukar

et al. 2023

View full text Add to dashboard Cite

show abstract

“…, 2020; Wu and Zhang, 2018); and optimising the structural flexibility (Xu et al. , 2020; Sapnken and Tamba, 2022). Other lines of thought are directed towards stability (He et al.…”

Section: Introductionmentioning

confidence: 99%

“…In general, GMs may be divided into univariate (Sapnken and Tamba, 2022) and multivariate models (Amber et al. , 2015, 2018; Arsel, 2014; Bento and Ramos, 2021; Bezuglov and Comert, 2016; Calvin Nsangou et al.…”

Section: Introductionmentioning

confidence: 99%

An optimal wavelet transform grey multivariate convolution model to forecast electricity demand: a novel approach

Sapnken,

Hamaidi,

Hamed

et al. 2023

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PurposeFor some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).Design/methodology/approachSpecifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.FindingsResults show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.Originality/valueThese interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

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Application of Main Controlling Factors for Quantitative Evaluation of a Favorable Carbonate Oil- and Gas-Bearing Area in the Pre-exploration Stage: Lianglitage Formation in the Central Uplift Belt of the Tarim Basin

Li,

Ran,

Tang

et al. 2024

Nat Resour Res

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Petroleum products consumption forecasting based on a new structural auto-adaptive intelligent grey prediction model

Cited by 11 publications

References 42 publications

Learning latent dynamics with a grey neural ODE prediction model and its application

Learning latent dynamics with a grey neural ODE prediction model and its application

An optimal wavelet transform grey multivariate convolution model to forecast electricity demand: a novel approach

Application of Main Controlling Factors for Quantitative Evaluation of a Favorable Carbonate Oil- and Gas-Bearing Area in the Pre-exploration Stage: Lianglitage Formation in the Central Uplift Belt of the Tarim Basin

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