Machine learning of redundant energy of a solar PV Mini-grid system for cooking applications

Solar PV mini-grids are increasingly being deployed in off-grid and island communities especially in sub-Saharan Africa (SSA) countries to meet household energy demand. However, one challenge of solar PV mini-grids for community energy supply is the mismatch between the PV energy generation and household energy demand. PV mini-grid energy generation is highest in the afternoon whilst household energy demand is highest in the mornings and evenings, but lowest in the afternoons. This mismatch creates redundant energy generation during peak sunshine hours when battery energy storage is full, leading to low profitability for mini-grid systems. In this study, four machine learning models have been applied on an installed 30.6 kW mini-grid system in Ghana to ascertain the level of the redundant energy. The study has revealed that redundant energy exists on the mini-grid, in the range of 56.98 - 119.86 kWh/day. Further analysis has shown that the redundant energy can support household cooking energy demand through sustainable thermal batteries. With the four machine learning (ML) models applied in predicting the redundant energy, the most accurate ML model, K-nearest Neighbour Regressor, had a root mean square error (RMSE) of 0.148 and a coefficient of determination (R2) value of 0.998.

Automated summary

Solar PV mini-grids are used in off-grid and island communities in sub-Saharan Africa to meet household energy demand. However, there is a mismatch between PV energy generation and household energy demand, leading to redundant energy. A study in Ghana revealed that the redundant energy can be used to support household cooking energy demand through sustainable thermal batteries. Machine learning models were applied to predict the redundant energy, and the most accurate model had a root mean square error of 0.148 and a coefficient of determination of 0.998.