Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Predicting Thermoelectric Power Plants Diesel/Heavy Fuel Oil Engine Fuel Consumption Using Univariate Forecasting and XGBoost Machine Learning Models

Monitoring and controlling thermoelectric power plants (TPPs) operational parameters have become essential to ensure system reliability, especially in emergencies. Due to system complexity, operating parameters control is often performed based on technical know-how and simplified analytical models t...

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Bibliographic Details
Main Authors: Elias Amancio Siqueira-Filho, Maira Farias Andrade Lira, Attilio Converti, Hugo Valadares Siqueira, Carmelo J. A. Bastos-Filho
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Energies
Subjects:
time series forecasting
power plants
fuel consumption
Box & Jenkins methodology
XGBoost
artificial neural networks
Online Access:https://www.mdpi.com/1996-1073/16/7/2942

Internet

https://www.mdpi.com/1996-1073/16/7/2942

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