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Renewable energy development is growing fast and is expected to expand in the next decades in West Africa as a contribution to addressing the power demand and climate change mitigation. However, the future impacts of climate change on solar PV and the wind energy potential in the region are still unclear. This study investigates the expected future impacts of climate change on solar PV and wind energy potential over West Africa using an ensemble of three regional climate models (RCMs). Each RCM is driven by three global climate models (GCMs) from the new coordinated high-resolution output for regional evaluations (CORDEX-CORE) under the RCP8.5 scenario. Two projection periods were used: the near future (2021–2050) and the far future (2071–2100). For the model evaluation, reanalysis data from ERA5 and satellite-based climate data (SARAH-2) were used. The models and their ensemble mean (hereafter Mean) show acceptable performance for the simulations of the solar PV potential, the wind power density, and related variables with some biases. The Mean predicts a general decrease in the solar PV potential over the region of about −2% in the near future and −4% in the far future. The wind power density (WPD) is expected to increase by about 20% in the near future and 40% in the far future. The changes for solar PV potential seem to be consistent, although the intensity differs according to the RCM used. For the WPD, there are some discrepancies among the RCMs in terms of intensity and direction. This study can guide governments and policymakers in decision making for future solar and wind energy projects in the region. [ABSTRACT FROM AUTHOR]

Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those 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 a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that 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 SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector. [ABSTRACT FROM AUTHOR]