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The annual yield of bifacial photovoltaic systems is highly dependent on the albedo of the underlying soil. There are currently no published data about the albedo of red soil in western Africa. In this study, the impact of the albedo of red soil in Ghana on the energy yield of bifacial photovoltaic systems is analysed. A bifacial photovoltaic simulation model is created by combining the optical view factor matrix with an electrical output simulation. For an exact simulation, the albedo of red soil at three different locations in Ghana is measured for the first time. The average albedo of every red soil is clearly determined, as well as the measurement span including instrumentation uncertainty; values between 0.175 and 0.335 were measured. Considering these data, a state-of-the-art bifacial photovoltaic system with an average of 19.8% efficient modules in northern Ghana can achieve an annual energy yield of 508.8 kWh/m2 and a bifacial gain of up to 18.3% in comparison with monofacial photovoltaic panels. To summarise, red soil in two out of three locations in Ghana shows higher albedo values than most natural ground surfaces and therefore positively impacts the annual yield of bifacial photovoltaic systems.
Ghana suffers from frequent power outages, which can be compensated by off-grid energysolutions. Photovoltaic-hybrid systems become more and more important for rural electrificationdue to their potential to offer a clean and cost-effective energy supply. However, uncertainties relatedto the prediction of electrical loads and solar irradiance result in inefficient system control and canlead to an unstable electricity supply, which is vital for the high reliability required for applicationswithin the health sector. Model predictive control (MPC) algorithms present a viable option to tacklethose uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts.This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA)algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM)model, and (d) a customized statistical approach for electrical load forecasting on real load data of aGhanaian health facility, considering initially limited knowledge of load and pattern changes throughthe implementation of incremental learning. The correlation of the electrical load with exogenousvariables was determined to map out possible enhancements within the algorithms. Results showthat all algorithms show high accuracies with a median normalized root mean square error (nRMSE)<0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMAalgorithm and the linear regression model show extreme error outliers of nRMSE >1, methods viathe LSTM model and the customized statistical approaches perform better with a median nRMSE of0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study isa favoring towards the LSTM model and the statistical approach, with regard to MPC applicationswithin photovoltaic-hybrid system solutions in the Ghanaian health sector.
Illegal Gold mining and water quality. A case study of River Offin in the Central Region of Ghana
(2018)
Ghana as a country in the west of Africa is naturally endowed with many rich natural resources some of which includes; Gold, diamond, bauxite, manganese, cocoa, etc. Currently, it’s the second leading producer of gold production in Africa after South Africa. Gold production has produced a lot of benefits to the nation but at the same time contributed to many negativities ranging from pollution (water and air), land degradation, ethnic conflicts and deforestation. Industrial gold mining itself was never a big issue that caused any panic in the country till illegal gold miners “Galamseyers” also commenced operating. The sole objectives of this research were to determine the level of some specific heavy metal (Mercury, Lead, Arsenic and Zinc) concentrations within the Offin River in Dunkwa-on-Offin, Buabenso, Ayanfuri and Nkotumso. Results after the research revealed that, Ayanfuri recorded the highest level of concentrations. Nkotumso followed in that order before Buabenso with Dunkwa-on-Offin recording the least concentrations. It also became clear later that, even though the degree of intensiveness of the illegal mining “Galamsey” operations could have been a main factor for the increased concentrations, another finding was that, towns or communities that recorded higher concentrations throughout the study also showed to be towns that made use of inorganic fertilizers on its farms. The Minerals and mining Act 2006 of Ghana was then analyzed and conclusion was that, though the country has very strong policies regarding illegal mining termed “Galamsey”, there seemed to be institutional loop holes that have not been strong enough to combat illegal mining in the country. In recommending policy directives to improve the river quality, six (6) institutions including the Ministry of Lands and Natural Resources, Ministry of Environment, Science and Technology(MEST), Minerals Commission of Ghana, Environmental Protection Agency, Forestry Commission and the water Resources Commission were identified with detailed recommended roles clearly spelt out for each institution.