SUSTAINDAM: Climate Impacts on Akosombo & Kpong Hydropower
Machine-learning modelling of how climate change affects hydropower generation at Ghana’s Akosombo and Kpong dams, using Random Forest to link climate variability to output under the SUSTAINDAM project.
The challenge
Ghana’s Akosombo and Kpong dams supply a large share of the country’s electricity, yet their output depends on inflows that are increasingly disrupted by climate variability. Understanding how a changing climate will affect hydropower generation is essential for energy security and dam operations.
Our approach
As modelling expert for RCEES on the SUSTAINDAM project, Ransford Wusah Bakuri developed a machine-learning approach — using Random Forest — to model the impact of climate on hydropower generation at the Akosombo and Kpong dams.
The work linked climate and hydrological variables to historical generation records, producing a data-driven basis for anticipating how future climate conditions could shape output.
Outcomes
The modelling delivered the project’s first objective: a validated Random Forest model relating climate to hydropower generation at Akosombo and Kpong.
It gives operators and planners an evidence base for managing climate risk to two of Ghana’s most important power assets.
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