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A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success

Abstract

Energy scenarios, relying on wide-ranging assumptions about the future, do not always adequately reflect the lock-in risks caused by planned power-generation projects and the uncertainty around their chances of realization. In this study we built a machine-learning model that demonstrates high accuracy in predicting power-generation project failure and success using the largest dataset on historic and planned power plants available for Africa, combined with country-level characteristics. We found that the most relevant factors for successful commissioning of past projects are at plant level: capacity, fuel, ownership and connection type. We applied the trained model to predict the realization of the current project pipeline. Contrary to rapid transition scenarios, our results show that the share of non-hydro renewables in electricity generation is likely to remain below 10% in 2030, despite total generation more than doubling. These findings point to high carbon lock-in risks for Africa, unless a rapid decarbonization shock occurs leading to large-scale cancellation of the fossil fuel plants currently in the pipeline.

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Fig. 1: The impact of project- and country-level variables on predicting the successful commissioning of past projects.
Fig. 2: The impact of selected features on the model’s prediction.
Fig. 3: The predicted success probabilities of African countries’ planned project pipelines.
Fig. 4: The contribution of individual features to the predicted realization of regional planned project pipelines.
Fig. 5: Africa’s current and predicted 2030 electricity capacity mix by fuel type.
Fig. 6: Comparison of the 2030 generation mix, as projected by extant RE-heavy scenarios and predicted by the present study.

Data availability

The data on power-generation assets from LiveData were obtained under licence from African Energy (https://www.africa-energy.com/live-data). The country-level data on macroeconomic variables were retrieved from the World Bank World Development Indicators (http://datatopics.worldbank.org/world-development-indicators/), on institutional variables from the World Bank World Governance Indicators (https://info.worldbank.org/governance/wgi/) and on countries’ regime types from the Polity IV Project of the Center for Systemic Peace (https://www.systemicpeace.org/polityproject.html). The data on feed-in-tariff introduction dates were retrieved from RE21 annual Global Status Reports and are publicly available from https://www.ren21.net/reports/global-status-report/. The data on fossil fuel subsidies were retrieved from the International Monetary Fund and are publicly available from https://www.imf.org/en/Topics/Environment/energy-subsidies. The data that support the figures and other findings of the study are available from the corresponding author upon reasonable request.

Code availability

In this study a gradient boosted tree model was built using LightGBM, which is an open-source free algorithm developed by Microsoft. This code is available from the corresponding author upon reasonable request.

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Acknowledgements

We thank B. Caldecott, A. Haney, C. Hepburn, W. Kruger and D. Tulloch for comments on the manuscript. G.A. is grateful for the financial support of her doctoral research offered by the ESRC Grand Union Doctoral Training Partnership, the Scatcherd European Scholarship from the University of Oxford and the 73 Scholarship Fund for Geography from Hertford College, Oxford, established through the generosity of the college’s alumni, P. Newman and M. Teversham. A.M. acknowledges the support received from the British Academy’s Sustainable Development Programme, specifically, award GF160016 - Making Light Work, which was key to conceiving the research idea.

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Contributions

A.M. conceived the original research idea and sourced the asset-level data. G.A. and P.A.T. designed the research approach and merged asset- and country-level data. G.A. built, validated and interpreted the machine-learning-based model and produced the visuals of its results. P.A.T. developed the map. A.M. provided an early draft. G.A. and P.A.T. wrote the manuscript.

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Correspondence to Galina Alova.

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Peer review information Nature Energy thanks Dan Marks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Tables 1–8, Figs. 1–9, Notes 1–4 and references.

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Alova, G., Trotter, P.A. & Money, A. A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success. Nat Energy 6, 158–166 (2021). https://doi.org/10.1038/s41560-020-00755-9

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