Machine Learning - Based Prediction and System Performance Modelling – A Case Study of Garissa Solar Power Plant in Kenya
Domain:
environment and energyRecord type:
paper
Creator:
IgnSooGeo
Publisher:
MDP
Host:
This study focused on the predictive models incorporating machine learning techniques that induce new dynamics for forecasting energy generation, enabling effective planning, financing, and system monitoring. The research developed a machine learning-based power generation prediction model tailored explicitly for Kenya's Garissa solar power plant. The selected model demonstrated a root mean squared error of 5.23 during evaluation, resulting in a prediction accuracy of 90.42%. This high accuracy indicates that the model can be relied upon for precise generation prediction, facilitating effective planning, and system performance monitoring
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