  The instability of power generation from national grids has led industries
(e.g., telecommunication) to rely on plant generators to run their businesses.
However, these secondary generators create additional challenges such as fuel
leakages in and out of the system and perturbations in the fuel level gauges.
Consequently, telecommunication operators have been involved in a constant need
for fuel to supply diesel generators. With the increase in fuel prices due to
socio-economic factors, excessive fuel consumption and fuel pilferage become a
problem, and this affects the smooth run of the network companies. In this
work, we compared four machine learning algorithms (i.e. Gradient Boosting,
Random Forest, Neural Network, and Lasso) to predict the amount of fuel
consumed by a power generation plant. After evaluating the predictive accuracy
of these models, the Gradient Boosting model out-perform the other three
regressor models with the highest Nash efficiency value of 99.1%.
