Predicting Fuel Consumption in Power Generation Plants using Machine Learning and Neural Networks
Domain:
environment and energyRecord type:
paperdataset
Creator:
NguAteAnsRoc
Host:
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%.
Similar
Predicting Failures Using Neural Networks and Quantum Optimization Techniques in Concentrated Solar Power Plants: A Case Study of Morocco Noor Ouarzazate ComplexSolar Photovoltaic Power Potential and Generation Forecasting in Nigeria using Machine LearningPredicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural NetworksPredicting Household Plastic Level Consumption Using Machine Learning and Artificial Intelligence (AI)Africa Power Generation PlantsDay-Ahead and Week-Ahead Solar PV Power Forecasting Using Deep Learning Neural Networks