Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks
This work addresses fuel management challenges for telecommunication operators using backup generators, but it is incremental as it applies existing machine learning methods to a specific domain.
The study tackled the problem of predicting fuel consumption in power generation plants to address issues like excessive consumption and pilferage, resulting in a Gradient Boosting model achieving a Nash efficiency of 99.1%.
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%.