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The electricity needs of populations in Cameroon are increasing and are still very inadequate. Companies, public buildings and households are facing frequent blackout which constrain development and social well-being. Therefore, the present work tried to forecast the electricity demand in the residential sector in Cameroon, in order to contribute significantly to the mastery of electricity consumption and highlight decision-makers in this sector. Six macroeconomics parameters covering the period 1994-2014 are used for these issues. Stationarity tests within gross domestic product, gross domestic product per capita, electricity consumption, population and numbers of subscribers and households respectively; reveal that all the series are I(1). Thus, the VAR (Vector Autoregressive) model has been retained to forecast the electricity demand until 2020. The cusum test and the cusum of squared test attest the stability of that model with a margin of error of 0.02%. Previsions are then more reliable and show that the electric request will skip from 1721 GWh in 2014 to more than 2481 GWh in 2020 approximatively, following a growing yearly rate of 5.36%. In order to reach its emergence, Cameroon ought to speed up its production in the domain of hydroelectric and thermal grid in order to meet the requirements in electric power in short and long term.

Highlights • The Grey and Vector autoregressive models are coupled to improve their accuracy. • Five economic and demographic parameters are included in the new hybrid model. • This new model is a reliable forecasting tool for assessing energy demand., Forecasting energy demand in general, and electricity demand in particular, requires the developing reliable forecasting tools that can be used to monitor the evolution of consumers’ energy needs more accurately. The proposed new hybrid GM(1,1)-VAR(1) model is meant for that purpose. The latter is based on the Grey and Vector autoregressive approaches, and makes it possible to predict future demand, by taking into account economic and demographic determinants with an exponential growth trend. With an associated APE of 1.5, a MAPE of 1.628%, and an RMSE of 15.42, this new model thus presents better accuracy indicators than hybrid models of the same nature. Also, it proves to be as accurate as some recent hybrid artificial intelligence models. The model is thus a reliable forecasting tool that can be used to monitor the evolution of energy demand.•The Grey and Vector autoregressive models are coupled to improve their accuracy.•Five economic and demographic parameters are included in the new hybrid model.•This new model is a reliable forecasting tool for assessing energy demand., Graphical abstract Image, graphical abstract

Cameroon is highly growing in energy as a whole, and in electricity in particular. This growth is expected to increase within the next years, thanks to the current emergence as well as the country’s major projects. Therefore, mastering electricity demand in the residential sector is one of State’s priorities. In fact, this falls under the development plan of the electricity sector by 2025. Therefore, this paper highlights the forecast of the electricity consumption regarding the residential sector in Cameroon. The new GM(1,1)-VAR(1) hybrid model which is based on the VAR and Grey models, is used for this purpose. Results from the new model show that the previsional GM(1,1)-VAR(1) model is strong and reliable, just like some recent and modern hybrid models. Electricity needs of the residential sector by 2025 are estimated at 2641.632 GWh, with a 1 . 628 % MAPE, and a 15.42 RMSE. Consequently, the new hybrid model should be a reliable previsional tool that makes it possible to monitor the evolution of electricity demand of the residential sector in cameroon.

The building structure is a filter that controls the heat exchanges between the exterior and the interior of the building. It plays an important role for the thermal comfort of the residents and the energy consumption of the building. The objective of this work is to analyse the thermal behaviour of the building structure in humid climates for various local building materials: wood, cinder block and clay brick. To achieve this, a numerical code was written in the MATLAB 2014a software to solve by the finite difference method implicit scheme, the three-dimensional heat transfer equation. The simulation of our dynamic model takes into account the convection on the external and internal surfaces as well as the diffusion in the material constituting the structure. The particularity of this study is that in addition to being a 3D model, it allows to appreciate the behaviour in the thermal bridges and in the interior geometric centre. After analysis, we recommended an envelope made of wood material in the littoral region.

Hydrogen demand should significantly increase in the next few years due to rapid growth of energy demand, the refinery industry’s growing needs and applications such as biofuel production. To meet the demand advanced processes are being developed throughout the world in a sustainability context. Among the most studied ones is high temperature electrolysis of steam using solar photovoltaic and thermal energy. In this work, a large scale hybrid solar hydrogen production system was designed and optimized. The system consists of the solid oxide steam electrolysis system coupled to a Photovoltaic array through a Direct Current converter and coupled to a Parabolic Trough Collector through a heat exchanger network optimized using a Generalized Reduced Gradient algorithm. Under local tropical conditions, the theoretical production rate of hydrogen per second and per day recorded from simulation of interconnected system based on optimum scenario are respectively 0.064 kg/s and 1843.2 kg/day. The exergy efficiency, land used and water consumption of system component was also performed.

This paper centres on the estimation of carbon dioxide emissions in a Cameroon thermal power plant called Dibamba Power Development Company, in such a way that they can be included as part of Cameroon energy sector inventory or used by the Dibamba Power Development Company to monitor its policy and technology improvements for mitigating climate change. We have estimated the emissions using national emission factors for the consumption of liquid fossil fuels and simulated a mitigation of these emissions till 2018 using alternative fossil fuels and carbon neutral model. The results show that energy demand and carbon dioxide emissions in 2012 are estimated to be 48.964 ktoe and 164.39 kt CO2 respectively. National emission factors for electricity generation are estimated to be 660.63 g/kWh. From 2012 to 2018, the thermal power plant will emit into the atmosphere 1298.42 kt CO2. These results also show that the use of alternative fuels will reduce 59.22 kt CO2 per year for the same period while the use of the carbon neutral model will reduce a total amount of 8.08 kt CO2. Finally, the total quantity of CO2 emission reduced for the period 2012 to 2018 will be 489.91 kt CO2.

Desorption isotherms of Afzelia, Ebony, Iroko, Moabi and Obeche have been experimentally found at 20, 30, 40, 50 and 60 °C by use of the salt method for a range of water activities from 0.06 to 0.97. The sorption capacity of these woods increases with the temperature for a given water activity. The experimental curves have been simulated by the GAB and BET models. The GAB model enables the representation of the whole desorption isotherms. Meanwhile, the BET model ensures a better representation of the experimental results for water activity lower than 0.35 with mean relative deviation of 2.9, 2.1, 1.3, 3.2 and 3.3% for respectively Afzelia, Ebony, Iroko, Moabi and Obecheand a better estimation of the water content corresponding to monolayer saturation.