A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights.

Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.

Unravelling the dynamics of human development and economic growth on crude oil production based on ARDL and NARDL models.

This paper estimates and establishes the causality between the Human Development Index (HDI), Gross Domestic Product (GDP), inflation and CO2 emissions on crude oil production (COP) in Cameroon from 1977 to 2019. To do so, the Augmented Dicky-Fuller and Zivot-Andrews stationarity tests, ARDL and NARDL modelling, as well as Toda-Yamamoto causality test are performed. Unlike previous studies on COP, this study incorporates the asymmetric impact (NARDL). The results indicate that CO2 emissions and GDP have a negative impact on COP in the long-run, while HDI and inflation have a positive impact in the short-run. GDP and HDI have a non-linear impact in the short run, while in the long-run inflation and CO2 emissions have a non-linear impact on COP. From these results, it is interesting to note that, in order to allow future generations to benefit from the oil windfall. The diversification of the Cameroonian economy, the control of inflation and the use of less polluting crude oil extraction technologies must be imperative.•A step-by-step procedure of the ARDL, NARDL and causality test is provided.•The multiplier effects of GDP, HDI, inflation and CO2 emissions on COP are simulated.•The impact of GDP and HDI on COP is non-linear.

Methodology for examining the relationship between oil rent and crude oil production: Evidence from Cameroon.

The relationship between oil rent and crude oil production remains unexplored in Cameroon. This study therefore aims to apply the Autoregressive Distributed Lag (ARDL) estimation technique and the Granger causality test using the Toda-Yamamoto procedure to capture the symmetric impact and causality links between oil rent and crude oil production in Cameroon. The study covers the period from 1977 to 2019, and includes crude oil prices, human development index (HDI) and corruption as other variables. The study indicates that there is a significant negative linear impact of crude oil production on oil rent and a bidirectional causality between oil rent and crude oil production. Finally, the price of crude oil, HDI and corruption are found to pass through production to influence oil rent. The results of this study will guide policy makers in managing and sustaining oil revenues for growth and prosperity.•The paper examines the linear impact of crude oil production on oil rent and the causal links between crude oil production and oil rent by incorporating crude oil prices, HDI and corruption.•Bidirectional causality between oil rent and crude oil production.•Convergence of crude oil price, HDI and corruption to crude oil production to influence oil rent.