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This research paper aims to examine the asymmetric impact of income and price on household consumption of kerosene in Cameroon.

The methodological approach consists of testing for stationarity using the augmented Dickey–Fuller and Andrews and Zivot tests, determining cointegration using nonlinear autoregressive distributed lag (NARDL) test approach and finally examining asymmetry using the Wald test.

Results of the stationarity tests reveal that variables are all integrated of order less than two I(2). The NARDL approach indicates that the (positive and negative) income shock and the positive price boom negatively influence consumption in the long- and short-run. The same is true for the negative price shock, but the latter remains insignificant. Furthermore, the Wald test carried out in the study confirms that the cumulative effects of the positive and negative income and price shocks are asymmetric.

The increase in the price of kerosene due to the lifting of subsidies has led to a decrease in household consumption and an unfortunate increase in the loss of tree cover in Cameroon. According to the results, this phenomenon will persist even if the price is reduced. Actions aimed at reducing its production at the expense of liquefied petroleum gas, electricity and renewable energy should be encouraged to limit the loss of vegetation cover. Thus, this study could contribute to solving the problem of deforestation and desertification in Cameroon.

This paper addresses the challenges associated with forecasting electricity consumption using limited data without making prior assumptions on normality. The study aims to enhance the predictive performance of grey models by proposing a novel grey multivariate convolution model incorporating residual modification and residual genetic programming sign estimation.

The research begins by constructing a novel grey multivariate convolution model and demonstrates the utilization of genetic programming to enhance prediction accuracy by exploiting the signs of forecast residuals. Various statistical criteria are employed to assess the predictive performance of the proposed model. The validation process involves applying the model to real datasets spanning from 2001 to 2019 for forecasting annual electricity consumption in Cameroon.

The novel hybrid model outperforms both grey and non-grey models in forecasting annual electricity consumption. The model's performance is evaluated using MAE, MSD, RMSE, and R², yielding values of 0.014, 101.01, 10.05, and 99% respectively. Results from validation cases and real-world scenarios demonstrate the feasibility and effectiveness of the proposed model. The combination of genetic programming and grey convolution model offers a significant improvement over competing models. Notably, the dynamic adaptability of genetic programming enhances the model's accuracy by mimicking expert systems' knowledge and decision-making, allowing for the identification of subtle changes in electricity demand patterns.

This paper introduces a novel grey multivariate convolution model that incorporates residual modification and genetic programming sign estimation. The application of genetic programming to enhance prediction accuracy by leveraging forecast residuals represents a unique approach. The study showcases the superiority of the proposed model over existing grey and non-grey models, emphasizing its adaptability and expert-like ability to learn and refine forecasting rules dynamically. The potential extension of the model to other forecasting fields is also highlighted, indicating its versatility and applicability beyond electricity consumption prediction in Cameroon.

For 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)).

Specifically, 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.

Results 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.

These 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.

In this study, a new neural differential grey model is proposed for the purpose of accurately excavating the evolution of real systems.

For 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.

The 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.

This 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.

Conventional statistical forecasting methods typically need a large sample size or the use of overly confident hypotheses, like the Gaussian distribution of the input data. Unfortunately, these input data are frequently scarce or do no not follow a normal distribution law. A grey forecasting model can be developed and used to predict energy consumption for at least four data points or ambiguous data based on grey theory. The standard grey model, however, may occasionally result in significant forecasting errors.

In order to reduce these errors, this paper proposes a hybrid multivariate grey model (namely Grey Modeling (1,N)) optimized by Genetic Algorithms with sequential selection forecasting mechanism, abbreviated as Sequential-GMGA(1,N). A real case of Cameroon's oil products consumption is considered to demonstrate the effectiveness of the proposed forecasting model.

The results show the superiority of Sequential-GMGA(1,4) when compared with the results of competing grey models reported in the literature, with a mean absolute percentage error as low as 0.02%.

Without changing the model's basic structure, the suggested framework completely extracts the evolution law of multivariate time series. Regardless of data patterns, Sequential-GMGA(1,4) actively enhances all model parameters over the course of each predicted timeframe. Consequently, Sequential-GMGA(1,4) improves forecast accuracy by resolving the discrepancy between the model's least sum of squares of prediction errors and the parameterization approach based on grey derivative.