Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks

TL;DR

This paper compares machine learning models to accurately predict fuel consumption in power plants, highlighting Gradient Boosting's superior performance with a 99.1% Nash efficiency.

Contribution

It evaluates and compares four machine learning algorithms for fuel consumption prediction, demonstrating the effectiveness of Gradient Boosting in this context.

Findings

Gradient Boosting achieved 99.1% Nash efficiency.

Neural Network performed well but was outperformed by Gradient Boosting.

The study provides a reliable predictive model for fuel consumption in power plants.

Abstract

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%.