Year: 2024
Type: article
Abstract: To achieve precise energy supply and energy-efficient operation of the steam system in the primary processing workshop of tobacco enterprises, a Random Forest prediction model is introduced in this study to forecast steam consumption.A correlation analysis is conducted to identify the factors that significantly impact steam load.These key features are then used to develop the Random Forest prediction model, with appropriate hyper parameters selected to enhance prediction accuracy.The results indicate that the Random Forest model offers high prediction precision and strong practical applicability, providing a basis for optimizing the scheduling of the steam system.
DOI: 10.18280/ijht.420538
Authors: Wei Gao, Kai Zhang, Feng Zheng, Xiaolei Liu, Mingqi Nan, Lei Yan, Yongchun Wen, Yuxuan Han, Hongxi Zhang
Institution: Hebei University of Architecture
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404424108