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The Future of Renewable Energy for Electricity Generation in sub-Saharan Africa

机译:撒哈拉以南非洲发电的可再生能源未来

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Energy transition in the last decade has experienced increased quota of renewable energy in the global energy mix. In sub-Saharan Africa (SSA), the transition from the fossil fuel to the renewable energy source has been gradual. The state of renewable energy in the region in the next decade is the focus of this study. This study uses a single-layer perceptron artificial neural network (SLP-ANN) to backcast from 2015 to 2006 and forecast from 2016 to 2020 the percentage of renewable energy for electricity generation, exempting the hydropower in the energy mix of the SSA based on historical data. The backcast percentage renewable energy mix was evaluated using known statistical metrics for accuracy measures. The root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) obtained were 0.29, 0.18, and 14.69 respectively. The result shows possibility of an increase in the percentage of renewable energy in the electricity sector in the region. In 2020, the percentage of renewable energy in sub-Saharan region is expected to rise to 4.13% with exclusion of the hydropower. With government policies encouraging the growth of the renewable energy as a means of power generation in the region, the predicted percentage and even more can be realized.
机译:过去十年的能源转型在全球能源组合中经历了可再生能源的额度增加。在撒哈拉以南非洲(SSA)中,从化石燃料到可再生能源的过渡一直是渐进的。未来十年该地区可再生能源的状态是本研究的重点。本研究采用单层感知人工神经网络(SLP-ANN)从2015年到2006年到2006年,并从2016年到2020年从2016年到2020年的发电能源的百分比,以历史为基础的SSA能量组合中的水电站数据。使用已知的统计指标评估次数百分比可再生能源混合物以获得精度措施。获得的根均方误差(RMSE),即获得的平均绝对偏差(MAD)和平均绝对百分比误差(MAPE)分别为0.29,0.18和14.69。结果表明,该地区电力部门中可再生能源百分比增加的可能性。在2020年,亚撒哈兰地区可再生能源的百分比预计将升至4.13%,排除水电。通过政府政策,鼓励可再生能源的增长作为该地区的发电方式,可以实现预测的百分比甚至更多。

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