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Access to electricity is vital in rural regions of Africa. There is substantial potential to utilise wind energy, yet investment in renewable energy projects is trivial because it has been insufficiently endorsed. As a result, most people living in remote regions of Africa rely entirely on diesel-powered generators, which expel pollutants in substantial amounts. These adversely affect the health of people and the environment. Sudan – one of Africa's largest countries – has a range of resources from which renewable energy could be generated, including favourable wind power generating conditions. This paper represents the first effort in literature to use a strategic perspective to explore how viable wind energy systems are in Sudan. It reports a study using the Hybrid Optimization Model for Electric Renewables (HOMER) across 21 diverse sites in Sudan. It identifies the optimal wind turbine system and best locations and analyses the costs involved and how much pollution could be prevented by implementing wind farms instead of diesel systems. The results establish that the optimal type of wind turbine across the investigated locations is the Enercon E−138 EP3. Wind farm use is expected to avoid almost 49 million kg/year of pollutants and reduce energy cost by US$0.91317/kWh. The optimal sites for utilising wind energy were Wawa, Dongola, Karima, Wadi Halfa and Al-Goled in order of suitability and their low costs of electricity and high wind speed records. Wind turbine pricing is predicted to reduce over time, so the study varied the cost of wind turbines to understand the impact on project economics. A reduction in wind turbine costs by 10 % lowered energy cost to between US$0.03681/kWh and US$0.04052/kWh. A further 25 % price drop reduced the cost of energy to between US$0.03067/kWh and US$0.03376/kWh. This study reveals the significant potential of integrating renewable energy into the energy mix of Sudan and provides an essential reference for wind farms in the energy sector across other developing countries. • Finding the ideal wind turbine system and sites for exploiting wind energy in Sudan. • Exploring the costs and evaded emissions using wind farms over diesel generators. • Investigating the impact of wind turbine price on the project economics. • The optimal wind turbine was determined to be Enercon E−138 EP3. • The best locations for harvesting wind energy are Wawa Dongola, Karima and Wadi Halfa. [ABSTRACT FROM AUTHOR]

Self-powered biomedical devices, which are the new vision of Internet Of Things (IOT) healthcare, are facing many technical and application challenges. Many research works have reported biomedical devices and self-powered applications for healthcare, along with various strategies to improve the monitoring time of self-powered devices or to eliminate the dependence on electrochemical batteries. However, none of these works have especially assessed the development and application of healthcare devices in an African context. This article provides a comprehensive review of self-powered devices in the biomedical research field, introduces their applications for healthcare, evaluates their status in Africa by providing a thorough review of existing biomedical device initiatives and available financial and scientific cooperation institutions in Africa for the biomedical research field, and highlights general challenges for implementing self-powered biomedical devices and particular challenges related to developing countries. The future perspectives of the aforementioned research field are provided, as well as an architecture for improving this research field in developing countries.

This study provides an overview of potential renewable energy (RE) projects for Africa. Pan African University master students were asked by their lecturer to characterize African projects they had in minds in uniform tables. Items of the project investment plan include project synopsis, the sponsors, market analysis and strategy, project scope, regulation and environmental information, project costs, financial projections, business model, and project innovativeness. Students' RE projects are assessed with emphasis put on employed business models and project innovativeness. Criticalities for the implementation of these projects are discussed in African contexts providing outlook for future investment opportunities in the African continent. The work provides insights from the local students' perspective for the various stakeholders interested in RE project investments in Africa. Useful suggestions formulated directly by young Africans are presented which may contribute to improved risks management when these or other potential RE projects will be deployed. Inputs from local people on how they understand the process of implementing RE projects in Africa might be interesting for investors seeking information about suitable ways for RE project deployment. Students characterize local contexts and identify numerous barriers for deployment of RE systems in Africa. Overall, this study explains the potential lying in renewable energy harvesting in Africa, seeks to emphasize major barriers for implementation of RE projects as well as investigates investment opportunities to implement economically feasible RE projects for cleaner and climate friendly energy future of this continent. [ABSTRACT FROM AUTHOR]

Abstract: In recent years, the quest for renewable and sustainable energy has been extensive while solar energy has been on the vanguard of sustainable alternative and renewable energy sources due to its clean nature and cost effectiveness for most human activities such as water pumping and electric power generation, amongst others. The off beam installation of Photovoltaic (PV) modules has posed a severe challenge to the optimal functioning of these PV cells despite the abundance of solar irradiation receivable in most African cities. This paper presents the Optimal Inclination Angles (OIA) for mounting PV modules in the absences of a mechanized or automated solar tracking device, for optimum yield in solar electricity generation for some selected African cities using the Photovoltaic Geographic Information Systems (PVGIS) dataset. The OIA of the selected African cities has been identified for optimal solar irradiation exploitation and if the modules are mounted on a horizontal plane, it is expected that considerable amount of solar irradiation would not be harnessed as it has been estimated using the difference from the Irradiation on OIA (Hopt) and the Irradiation on horizontal plane (Hh), whose difference shows that the northern African cities, Algiers, Rabat and Tripoli, are seen to have high levels in unutilized solar irradiation of 780 Wh/m2, 760 Wh/m2 and 680 Wh/m2 respectively while Harare, Lusaka, Maiduguri, Khartoum, Maputo and Luanda would have considerably high levels in untapped solar irradiation of 360Wh/m2, 330 Wh/m2, 180 Wh/m2, 260 Wh/m2, 570 Wh/m2 and 80 Wh/m2 respectively if PV modules are mounted on horizontal plane. However cities such as Bangui, Abidjan and Mogadishu have quite low levels in unexploited solar irradiation of 40 Wh/m2, 70 Wh/m2 and 10 Wh/m2 respectively when PV modules are mounted on horizontal plane. These differences show the amount of solar irradiation which if adequately harnessed, adds to the solar energy potentials of the region. [Copyright &y& Elsevier]

Beekeeping in Africa has been practiced for many years through successive generations and along inherited patterns. Beekeepers continue to face challenges in accessing consistent and business-driven markets for their bee products. In addition, the honeybee populations are decreasing due to colony collapse disorder (CCD), fire, loss of bees in swarming, honey buggers and other animals, moths, starvation, cold weather, and Varoa mites. The main issues are related to un-controlled temperature, humidity, and traditional management of beekeeping. These challenges result in low production of honey and colony losses. The control of the environmental conditions within and surrounding the beehives are not available to beekeepers due to the lack of monitoring systems. A Smart Beehive System using Internet of Things (IoT) technology would allow beekeepers to keep track of the amount of honey created in their hives and bee colonies even when they are far from their hives, through mobile phones, which would curtail the challenges currently faced by the beekeepers. However, there are challenges in the design of energy-efficient embedded electronic devices for IoT. A promising solution is to provide energy autonomy to the IoT nodes that will harvest residual energy from ambient sources, such as motion, vibrations, light, or heat. This paper proposes a Self-Powered Smart Beehive Monitoring and Control System (SBMaCS) using IoT to support remote follow-up and control, enhancing bee colonies' security and thus increasing the honey productivity. First, we develop the SBMaCS hardware prototype interconnecting various sensors, such as temperature sensor, humidity sensor, piezoelectric transducer—which will work as a weight sensor—motion sensor, and flame sensor. Second, we introduce energy harvesting models to self-power the SBMaCS by analyzing the (i) energy harvested from adult bees' vibrations, (ii) energy harvesting through the piezoelectric transducer, and (iii) radio frequency energy harvesting. Third, we develop a mobile phone application that interacts with the SBMaCS hardware to monitor and control the various parameters related to the beehives. Finally, the SBMaCS PCB layout is also designed. SBMaCS will help beekeepers to successfully monitor and control some important smart beekeeping activities wherever they are using their mobile phone application. [ABSTRACT FROM AUTHOR]

Global solar radiation prediction is highly desirable for multiple energy applications, such as energy production and sustainability, solar energy systems management, and lighting tasks for home use and recreational purposes. This research work designs a new approach and investigates the capability of novel data intelligent models based on the self-adaptive evolutionary extreme learning machine (SaE-ELM) algorithm to predict daily solar radiation in the Burkina Faso region. Four different meteorological stations are tested in the modeling process: Boromo, Dori, Gaoua and Po, located in West Africa. Various climate variables associated with the changes in solar radiation are utilized as the exploratory predictor variables through different input combinations used in the intelligent model (maximum and minimum air temperatures and humidity, wind speed, evaporation and vapor pressure deficits). The input combinations are then constructed based on the magnitude of the Pearson correlation coefficient computed between the predictors and the predictand, as a baseline method to determine the similarity between the predictors and the target variable. The results of the four tested meteorological stations show consistent findings, where the incorporation of all climate variables seemed to generate data intelligent models that performs with best prediction accuracy. A closer examination showed that the tested sites, Boromo, Dori, Gaoua and Po, attained the best performance result in the testing phase, with a root mean square error and a mean absolute error (RMSE-MAE [MJ/m2]) equating to about (0.72-0.54), (2.57-1.99), (0.88-0.65) and (1.17-0.86), respectively. In general, the proposed data intelligent models provide an excellent modeling strategy for solar radiation prediction, particularly over the Burkina Faso region in Western Africa. This study offers implications for solar energy exploration and energy management in data sparse regions. [ABSTRACT FROM AUTHOR]