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This article proposes a comparison and classification of PV system architectures with the aim of limiting the impact of the partial shading phenomenon which remains one of the most harmful defects during the production of electrical energy with significant consequences on output power, current and voltage. the methodological approach used consists of analyzing in depth the main most recent architectures developed in the current literature with their advantages and disadvantages; then define five partial shading scenarios for different irradiance levels (1000W/m2; 900W/m2; 700W/m2; 500W/m2; 300W/m2), which will then be immediately applied to five other proposed architectures: SP (serial-parallel), BL (Bridge-Link); HC (Honey Comb); TCT (Total-Cross-Tied); TSPL (Triple Series Parallel Ladder); the values obtained at the output with each of its architectures will be used for an in-depth descriptive analysis with PCA (principal component analysis) in statistics but to carry out a comparative analysis between the architectures. All with a Matlab Simulink 2022.b software environment. The results obtained offer a strong positive correlation for TCP and TSPL architectures with better weight compared to other architectures. All in accordance with the IEEE-519-2022 standard. This work is therefore positioned as a contribution to the optimization of the performance of electrical energy production through the use of PV systems which today represent widely used alternatives in the renewable energy register.

This study introduces a ground-breaking approach, the Whale Optimization Algorithm (WOA)-based multivariate exponential smoothing Grey-Holt (GMHES) model, designed for electricity price forecasting. Key features of the proposed WOA-GMHES(1,N) model include leveraging historical data to comprehend the underlying trends in electricity prices and utilizing the WOA algorithm for adaptive optimization of model parameters to capture evolving market dynamics. Evaluating the model on authentic high- and low-voltage electricity price data from Cameroon demonstrates its superiority over competing models. The WOA-GMHES(1,N) model achieves remarkable performance with RMSE and SMAPE scores of 12.63 and 0.01 %, respectively, showcasing its accuracy and reliability. Notably, the model proves to be computationally efficient, generating forecasts in

In this study, the control volume method is used to determine the air temperature at the outlet of an air-to-ground heat exchanger. Its implementation consists in dividing the duct of the ground-air heat exchanger into micro-volumes of identical size. An energy balance is then established for each micro-volume. The input parameters used to implement this model are related to the city of Yaoundé in the equatorial zone. The results show that when the total length of the air-to-ground heat exchanger duct varies between 0 and 100 m, the air temperature at the outlet also varies between 34.5 and 24 °C. The air-to-ground heat exchanger operates in cooling mode. As the length of the air-to-ground heat exchanger duct increases, the temperature of the air at the outlet of the air-to-ground heat exchanger decreases, approaching that of the ground. Based on the results obtained using the control volume model, the minimum total length of air-to-ground heat exchanger duct recommended for this zone is 40 m. Admittedly, air pressure drops, air humidity and the geometry of the air-to-ground heat exchanger are aspects that have not yet been taken into account in the implementation of this model. Nevertheless, the control volume method can be used to optimise the parameters influencing the thermal performance of an air-to-ground heat exchanger. • The control volume method is implemented here by dividing the air-to-ground heat exchanger duct into identical micro-volumes and then establishing an energy balance for each micro-volume; • In this work, the control volume method was used to optimise the total length of the duct of a ground air heat exchanger installed in an equatorial zone; • Some important aspects such as air pressure drops, air humidity, and the geometry of the air-to-ground heat exchanger are not yet taken into account in the implementation of the control volume method.

Data presented in this paper were collected with the aim of describing the mean monthly temperature evolution of a lateritic soil in an equatorial zone during the dry season. The site is located in Cameroon and has the following geographical coordinates: latitude 3.879444° North and longitude 11.468333° East. Data collection involved measuring the water temperature and depth of existing wells at the site and determining their geographical coordinates. The data were collected manually. The temperatures of the various wells were measured daily throughout February 2022, and the average monthly temperatures were then calculated. These are presented in this paper, along with the other measured data. Several pieces of equipment were used for this purpose. An infrared laser thermometer was used to measure the temperatures of the various wells. A UTM GPS (Universal Transverse Mercator Global Positioning System) unit was used to determine the geographical position of each well in the study area. A plumb line was used to measure the depths of the various wells. The length of the plumb line inserted into the well was measured using an ankle brace. An infrared rangefinder was used to measure the distance, surface area, and volume of each well. The Coordinate Converter application was used to convert the east longitude and north latitude of each well in the study area into decimal degrees. Microsoft Excel was used to draw up tables of measured and converted data and to plot a graph. Surfer software was used to produce isothermal maps. The data collected provide information on changes in the average monthly temperature of the soil at the site as a function of its depth and geographical position. This information facilitates the installation of heat pumps and other heat exchangers to meet the heating and cooling needs of buildings in the area. Additionally, this data can be reused to identify suitable locations for the construction of new water wells and buildings at the site.

Abstract 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.

In the wood processing industry, sawmills generate huge amounts of sawdust as a renewable energy source. This paper aims to describe the production process and investigate the physico-chemical properties of mixed briquettes from two tropical sawdust species, including Iroko (Chlorophora excelsa) and Padouk (Pterocarpus soyauxii) and starch binder. The sawdust from each wood was dried, mixed in the ratios of 100:0; 75:25; 50:50; 25:75; 0:100 and then carbonised in the pyrolyser. The biochar was ground, mixed in the combinations of 75:25; 50:50; 25:75 and compacted using a manual hydraulic press. Proximal and ultimate analyses were applied to determine the combustion characteristics of the briquettes produced according to ASTM standards and Van Kreleven models. The results of briquette production showed that pure Padouk sawdust (P) has a high yield (18.4%), while mixed sawdust (C’) has a lower yield (12.4%). The density varies from 0.28 g/cm 3 to 0.53 g/cm 3 and the ash content from 3.48% to 7.46%. The gross calorific value of the briquettes produced varies from 22.623 MJ/kg to 24.719 MJ/kg. The highest Gross Calorific Values (GCV) obtained on Padouk briquettes indicates their high content of extractables. Ultimate analysis of the samples showed that the briquettes were highly carbonaceous. The Van Krevelen diagram shows an atomic H/C ratio of less than 1.5 and an O/C ratio of less than 0.8 which indicate that the briquettes are highly flammable. The briquettes produced are suitable as an environmentally friendly alternative energy source as they have low moisture and ash content (below 10%), and high calorific values.

Over the period 2008-2019, this study examines the linear impact of petroleum product (PP) consumption in Chad, PP prices, the vehicle fleet and urbanisation on CO2 emissions on the one hand, and on the other, it determines the causal links that exist between the various variables studied. This study opts for the augmented Dickey-Fuller and Phillips-Perron tests to verify the stationarity of the variables. The ARDL model is then estimated and diagnostic tests are performed to confirm the validity of the model. To confirm the existence of long-term relationships, the bounds test was applied. Finally, the Toda-Yamamoto causality test was used to capture the influences present between the series under study. The main results show that, in the long term, a 1% increase in gasoline consumption would lead to a 1.03% increase in CO2 emissions and the linear impact of diesel consumption is positive and insignificant on CO2 emissions. In the short term, gasoline and diesel consumption have a positive and insignificant impact on CO2 emissions. In terms of causality, there is a unidirectional causality from gasoline consumption to CO2 emissions and a bidirectional causality between CO2 emissions and diesel consumption. This study is the first to simultaneously link CO2 emissions, PP consumption, PP prices, the vehicle fleet and urbanisation in general, and particularly in the case of Chad. It therefore adds to the literature on the simultaneous relationship between CO2 emissions, PP consumption, the car fleet and urbanisation in a global and restricted context. This study could guide Chadian oil pollution management decision-makers in adopting policies related to the effects of PP consumption in the road transport sector.

This paper explores the asymmetric and symmetric influence of the human development index (HDI), economic growth, CO2 emissions and inflation on crude oil production (COP) in Cameroon from 1977 to 2019. The Zivot-Andrews and Augmented Dickey-Fuller tests verified stationarity, the ARDL and NARDL models were applied to assess asymmetric and symmetric effects and finally, causality was established by the Toda-Yamamoto test. The results conclude that economic growth and CO2 emissions have a negative impact on COP in the long term, while in the short term the HDI and inflation have a positive impact. The presence of an asymmetric influence of HDI and economic growth in the short term, and an asymmetric influence of CO2 emissions and inflation on COP in the long term. A two-way connection between economic growth and COP. A unidirectional link between the HDI, CO2 emissions and inflation on COP. As a result of these findings, policies have been formulated to help Cameroon achieve crude oil production that is beneficial to social and economic development while respecting environmental standards.

Grey systems theory can be used to predict the evolution of a system with insufficient information. Unfortunately, the most used version of the grey model (GM), namely, GM(1,1), works best when the system series have an increasing exponential rate. In any other case, the GM(1,1) produces inaccurate predictions. In this paper, we examine the mathematical formulation of the conventional GM(1,1) in order to propose a new GM that addresses its shortcomings through a new time response function. Examples are presented to demonstrate the flexibility and accuracy of the new model when implemented with series of various natures. Comparisons with other intelligent GM(1,1) show that the proposed model performs better than the reference models.

This study aims to estimate the demand for petroleum products (PP) in the Chadian road sector by 2030 and to determine which of the two models used is the most efficient. The methodology is based on two optimised Grey models, namely: the Sequential-GM(1,N)-GA and NeuralODE-GM(1,1) models. These models reduce forecasting errors compared with the conventional Grey model. The forecasts confirm that both models are robust, with MAPEs of 1.16% and 2.5% respectively for gasoline and diesel obtained with the Sequential-GM(1,N)-GA, and 3.3% and 4.8% respectively for gasoline and diesel obtained with the NeuralODE-GM(1,1). We note that the Sequential-GM(1,N)-GA is more robust than NeuralODE-GM(1,1) with regard to MAPEs. The estimated consumption needs for gasoline and diesel in the road transport sector by 2030 are 294376818.5 and 381570061.5 litres respectively for the Sequential-GM(1,N)-GA and 264376818.5 and 375570061.5 litres for the NeuralODE-GM(1,1). Based on these results, securing the supply of PP in the road transport sector requires the development of the downstream petroleum sector. The development of alternative energies and the acquisition of hybrid vehicles. A policy encouraging mass transport in urban areas can considerably reduce energy consumption in this sector. This study adds to the literature through the simultaneous use of two new optimised grey models and their comparison in terms of predicting demand for PP in the Chadian road transport sector.

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

It is crucial to develop highly accurate forecasting techniques for electricity consumption in order to monitor and anticipate its evolution. In this work, a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)) is proposed. A linear corrective term is included in the conventional GMC(1,N) structure, parameter estimation is carried out in a manner consistent with the modelling process, and an iterative technique is used to get the cumulated forecasting function of ODGMC(1,N). As a result, the forecasting capacity of ODGMC(1,N) is more reliable and its stability is enhanced. For validation purposes, ODGM(1,N) is applied to forecast Cameroon's annual electricity demand. The results show that the novel model scores 1.74% MAPE and 132.16 RMSE and is more precise than competing models. • ODGMC(1,N) corrects the linear impact of t on the forecasting performance. • Wavelet transform is used to remove irrelevant information from input data. • The proposed model can be used to track annual electricity demand.

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.

This paper proposes an optimized wavelet transform Hausdorff multivariate grey model (OWTHGM(1,N)) that addresses some of the weaknesses of the conventional GM(1,N) model such as inaccurate prediction and poor stability. Three improvements have been made: First, all inputs are filtered using a wavelet transform; second, a new time response function is established using the Hausdorff derivative; and finally, the use of Rao's algorithm to optimise the model's parameters as well as the ξ-order accumulated value of the observation data described by the Hausdorff derivative. In order to demonstrate the effectiveness of OWTHGM(1,N), it is applied to predict CO2 emissions from road fuel combustion. The new model scores 1.27% MAPE and 79.983 RMSE, and is therefore more accurate than competing models. OWTHGM(1,N) could therefore serve a reliable forecasting tool and used to monitor the evolution of CO2 emissions in Cameroon. The forecast results also serve as a sound foundation for the formulation of energy consumption strategies and environmental policies.• Modification, extension and optimization of grey multivariate model is done.• The model is very generic can be applied to a wide variety of energy sectors.• OWTHGM(1,N) is a valid forecasting tool that can be used to track CO2 emissions.

This research adds to the assessment of the causal relationships between residential electricity consumption and its explanatory factors. Johansen cointegration, autoregressive distributed lag model, the vector error correction model, and Wald causality tests are used. In both the short and long run, a unidirectional causality towards electricity consumption is identified. The sustainable development of the electricity sector in Cameroon, as defined by the national development strategy for 2030 and the long-term electricity sector development plan for 2030, requires an increase in GDP per capita, the adoption of a compact less energy-intensive urban model, the strengthening of control over the evolution of the number of subscribers on the distribution networks, and the reduction of CO2 emissions from thermal electricity generation.

This paper presents a theoretical evaluation of the prices of mixed briquettes produced from coconut shells (CCS), banana peels (BNP), rattan waste (RWT), and sugarcane bagasse (SGC) and, on the other hand, an analysis of the economic viability of their use as a replacement for conventional household fuels (liquefied petroleum gas, fuelwood, and wood charcoal) in households in Cameroon. The investigation was carried out using the life cycle cost method on a typical household over a ten-year period with annual cooking energy requirements of 950 kWhth. The SGC–CCS and SGC–RWT mixed briquettes with ratios higher than 7.75% and 11.1%, respectively, have prices lower than EUR 0.063/kWhth. The Present Value of the Net Benefit is positive for the use of SGC–CCS and SGC–RWT mixed briquettes. The results show that by making the right mixes of residues, it is possible to obtain biomass briquettes that are less expensive than conventional fuels.

In this study, a techno-economic feasibility analysis of hybrid renewable energy systems for four household categories in rural areas of Chad was studied based on the multi-criteria assessment technique. The problem of this study is to know the best optimal solution in the technical and economic feasibility study of the decentralized mini-grids for the rural electrification of isolated villages in Chad. The main objective of the work is to assess technically, economically and environmentally the feasibility of six scenarios of hybrid systems in five isolated sites in Chad. The performance analysis involved six scenarios of possible hybrid solutions while achieving a supply-demand balance for sustainable electrification of the remote villages, using the HOMER software. The results have shown that the optimum combination of the hybrid system was the photovoltaic/battery system with a Net Present Cost (NPC) of US $ 328,146 and it was found at Etena village. The photovoltaic/Wind/Diesel/Battery hybrid configuration was the least optimum system and it has appeared in Mandelia village. In terms of energy cost, the lowest Levelized Cost of Energy (LCOE) was estimated at US $ 0.236/kWh in a photovoltaic/Wind/Battery configuration at Koundoul site and the highest costs US $ 0.363/kWh in the photovoltaic/Battery configuration at the Linia site. It is established that hybrid solutions can be developed to make electricity available and accessible to the population of the remote rural areas in Chad. However, it is imperative that the local government must subsidize the diesel price to promote the adaptation of the abundant renewable solutions.

Cameroon is a country in Central Africa that relies heavily on hydropower, fossil fuels, solar, and biomass for its energy needs. However, the unstable and intermittent nature of these energy sources makes them unreliable, and there is a pressing need for a more secure and sustainable energy supply. Geothermal energy, which is abundant in Cameroon due to its favorable geological characteristics, has not been fully explored as a potential energy source. This study is aimed at providing a comprehensive review of the current status and future prospects of geothermal energy in Cameroon, based on publications related to geothermal energy in Cameroon, geological, and geophysical studies. The objectives of this study are to analyze the existing literature on geothermal energy in Cameroon, to identify the challenges and opportunities associated with geothermal energy development, and to make recommendations for future research and policy decisions. The results indicate that geothermal energy in Cameroon is still in its infancy, with limited research and development in the field. However, the country has geothermal potential, particularly in the Adamawa and Cameroon volcanic line (CVL) areas. The review highlights the challenges and barriers to geothermal energy development in Cameroon, including limited financial resources, technical expertise, and regulatory frameworks. The findings of this study suggest that Cameroon has significant potential for geothermal energy development, and that further exploration and investment in this area could contribute significantly to a more secure and sustainable energy supply in the country.

Accurate mid- and long-term petroleum products (PP) consumption forecasting is vital for strategic reserve management and energy planning. In order to address the issue of energy forecasting, a novel structural auto-adaptive intelligent grey model (SAIGM) is developed in this paper. To start with, a novel time response function for predictions that corrects the main weaknesses of the traditional grey model is established. Then, the optimal parameter values are calculated using SAIGM to increase adaptability and flexibility to deal with a variety of forecasting dilemmas. The viability and performance of SAIGM are examined with both ideal and real-world data. The former is constructed from algebraic series while the latter is made up Cameroon's PP consumption data. With its ingrained structural flexibility, SAIGM yields forecasts with RMSE of 3.10 and 1.54% MAPE. The proposed model performs better than competing intelligent grey systems that have been developed to date and is thus a valid forecasting tool that can be used to track the growth of Cameroon's PP demand. • The ability of SAIGM enhances the forecasting power of intelligent grey models to fully extracting the laws of a system, no matter the data specifications. • SAIGM is extended to include quasi-exponential series by addressing structural flexibility and parametrization concerns. • Input attributes determination and data preprocessing are not required for the proposed model.

Forecasting energy consumption is a major concern for policymakers, oil industry companies, and many other associated businesses. Though there exist many forecasting tool, selecting the most appropriate one is critical. GM(1,1) has proven to be one of the most successful forecasting tool. GM(1,1) does not require any specific information and can be adapted to predict energy consumption using a minimum of four observations. Unfortunately, GM(1,1) on its own will generate too large forecast errors because it performs well only when data follow an exponential trend and should be implemented in a political-socio-economic free environment. To reduce these short-comings, this paper proposes a new GM(1,n) convolution model optimized by genetic algorithms integrating a sequential selection mechanism and arc consistency, abbreviated Sequential-GMC(1,n)-GA. The new model, like some recent hybrid versions, is robust and reliable, with MAPE of 1.44%, and RMSE of 0.833. • Modification, extension and optimization of grey multivariate model is done. • The model is very generic can be applied to a wide variety of energy sectors. • The new hybrid model is a valid forecasting tool that can be used to track the growth of households’ energy demand.

Forecasting energy consumption is a major concern for policymakers, oil industry companies, and many other associated businesses. Though there exist many forecasting methodologies, selecting the most appropriate one is critical. GM(1,1) has proven to be one of the most successful forecasting tool. GM(1,1) does not require any specific information and can be adapted to predict energy consumption using a minimum of four observations. Unfortunately, GM(1,1) on its own will generate too large forecast errors because it performs well only when data follow an exponential trend and should be implemented in a political-socio-economic free environment. To reduce these errors, this study proposes a new GM(1,n) convolution model optimized by genetic algorithms integrating a sequential selection mechanism and arc consistency, abbreviated Sequential-GMC(1,n)-GA. Practically, the proposed approach, on one hand, highlights the forecast for petroleum products consumption in Cameroon's household sector. On the other hand, it estimates the amount of CO2 that would be reduced if petroleum products in this sector were switched to clean energy. The new model, like some recent hybrid versions, is robust and reliable, according to the results. Households petroleum products needs by 2025 are estimated to be 150,318 kilo tons of oil equivalent with MAPE of 1.44%, and RMSE of 0.833. Therefore, households GHG emissions would be reduced by 733.85 kilo tons of CO2 equivalent if clean energy was used instead of petroleum products. As a result, the new hybrid model is a valid forecasting tool that can be used to track the growth of Cameroon's household energy demand.

This article highlights a comparison and classification of six algorithms for extracting reference currents used in the context of harmonic compensation for the case of a TLC hybrid filter on a stochastic electrical network. With the ultimate goal of determining the most robust and reliable algorithm, the electrical network presents four variable configurations such as, operation in sinusoidal and balanced voltage, sinusoidal and unbalanced voltage, balanced and disturbed voltage, or even in unbalanced voltage and disrupts. Firstly, a presentation of these six algorithms or methods identified in the time domain will be made with injections of the fifth-order harmonics taken in positive sequences during the execution or compilation of the six algorithms for the four network operating hypotheses. And in a second step, a cross-comparison of the performances on the basis of indicators such as the THD (harmonic distortion rate) and UFI (unbalance factors) required by the IEEE 519–2014 and EN 50160 standard, then a cross-validation is based on some results available in the literature. These results will now facilitate the choice of the most appropriate algorithm depending on the nature of the electrical network in force according to the case studies.

This work aims at reinforcing simultaneously the coefficient of performance (COP) and the exergetic coefficient of performance (ECOP), in order to improve the operation of an absorption chiller to be used in tropical areas. It uses a new method based on the determination of variable one-line matrix that allows to find the NH3 mass fraction of NH3-NaSCN solution on each branch of the system.This matrix is obtained by substitution between the empirical formulae of NH3 and NH3-NaSCN from two different approaches, with the aim of making the current model more simple and less complex than those commonly used by other researchers. The approach developed is a direct digital method, easy to implement and allowing to find and understand some hidden functions of the black boxes of several energy simulation softwares, such as the Engineering Solver Equation (EES). The modeling of the system is carried out in Matlab to predict the temperatures and mass flows that can upgrade the system. The purpose is to contribute to the improvement and commissioning of an absorption chiller operating at thermal comfort temperatures in two cities in Cameroon: Douala and Yaoundé. The results show that the temperatures in the generator, condenser and absorber for which the COP and ECOP are maximum are respectively [92°C; 100°C]; 35°C, and [35°C; 40.8°C], and those of the mass flow rates of the refrigerant leaving the generator and condenser are respectively [0.44 kg/s; 0.86 kg/s] and 0.98 kg/s. The evaporator does not show these remarks. The simulation results can be used for thermodynamic optimisation of the cooling capacity (CC) and reduction of electrical energy consumption of the current system.

The mastery of demand for electricity in Cameroon is one of the concerns of the State, which is part of the development plan for the electricity sector by 2025. Thus, this paper identifies the factors that influence the electricity sector and mentions positive government actions. We use in this work data on the basis of previous work on one hand and a survey on the other hand in order to reinforce the analysis. The survey was conducted on a sample of 3,000 households in Douala in order to identify socio-economic factors that influence the electricity sector. For this, face-to-face approach was chosen. Households were randomly selected, then a questionnaire was submitted to them, assisting them in their response options. Results show that though certain factors have a positive influence on the electricity sector, Cameroon's current electricity system still remains unsustainable. A comprehensive view of how various factors influence the electricity sector in Cameroon would help in understanding the challenges for the future development of the sector. Government policies in this area would be more enlightened and undergo reorganization. Different models of electricity consumption could thus be formulated and adopted in order to predict the potential impacts of changes in planning.

Data presented on this paper are from the collection on the experimental site during a 12 months period, corresponding to the two seasons of the year. The site is located in the city of Douala in Cameroon, in a hot and humid zone. The experimental premises are located above the ground floor of a storey-building. They are built of 20 cm thick hollow breezeblocks with interior and exterior cement plastering, and they consist of the following: an open uninhabited room; a closed uninhabited room; and an inhabited room. The data acquisition system was achieved by the thermo-hygrometers and the anemometer placed in the rooms in accordance with the ASHRAE 55 standard [1], on instruments for measuring thermal comfort parameters in buildings. Collected data consists of the following: temperature, relative humidity and air velocity outside the site; the temperature and relative humidity inside the different rooms. These parameters are collected with a one-hour time step. The matlab software is used to calculate the maximum, minimum, average and standard deviation for each measured parameter. several areas are used for data processing: the search for causal links between the climatic parameters of the site, and those of the indoor environments of the buildings; data prediction on a one year's history basis; the impact of each experimental scenario on the thermal behaviour of the room; the assessment of the heat transfer in the room; the evaluation of the potential for energy savings in the room.

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.

This work is an application of experimental temperature data previously collected in a residential building in Douala, Cameroon, in order to analyze thermal discomfort. The data was collected according to three occupancy scenarios over 12 month period using thermohygrometer sensors. The temperature data are analysed in comparison with the comfortable temperature range from 24°C to 28°C. The degree hour (DH) method was used to assess the severity of thermal discomfort in a hot and humid climate. The results reveal that the open C1, closed C2 and inhabited C3 rooms corresponding to scenarios C1, C2 and C3 respectively, have 7270.6°H, 9063.9°H and 10023°H. The inhabited room C3 has the largest DH and although the room C1 has the smallest DH, the latter largely exceeded the tolerable limit value of 1250°H set by the RE2020 Environmental Regulations. Results from this work can serve in building modelling for researchers and architects to act for the alleviation of thermal discomfort in regions with hot and humid climate.