Research on Coal Production Cost Prediction Based on PCA-SSA-SVR

Shuntang Zhang, Zhenyang Shi, Lihua Hu, Guojun Zhang

2023

Abstract

This paper starts from the research perspective of lean market-oriented management mechanism within coal enterprises, establishes key influencing factors indicators in terms of environment, technical equipment and organizational management, and builds a cost prediction model based on PCA-SSA-SVR, and compares it with multiple regression analysis and PCA-BP prediction model, the results show that the proposed model has outstanding It has the advantages of avoiding dimensional disasters, overcoming the shortcomings of relying on empirical debugging penalty coefficients and kernel function parameters, and the prediction accuracy meets the requirements, which can provide a basis for modern coal enterprises to formulate labour quotas and cost control plans.

Paper Citation

in Harvard Style

Zhang S., Shi Z., Hu L. and Zhang G. (2023). Research on Coal Production Cost Prediction Based on PCA-SSA-SVR. In Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT; ISBN 978-989-758-677-4, SciTePress, pages 483-487. DOI: 10.5220/0012286300003807

in Bibtex Style

@conference{anit23,
author={Shuntang Zhang and Zhenyang Shi and Lihua Hu and Guojun Zhang},
title={Research on Coal Production Cost Prediction Based on PCA-SSA-SVR},
booktitle={Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT},
year={2023},
pages={483-487},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012286300003807},
isbn={978-989-758-677-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Seminar on Artificial Intelligence, Networking and Information Technology - Volume 1: ANIT
TI - Research on Coal Production Cost Prediction Based on PCA-SSA-SVR
SN - 978-989-758-677-4
AU - Zhang S.
AU - Shi Z.
AU - Hu L.
AU - Zhang G.
PY - 2023
SP - 483
EP - 487
DO - 10.5220/0012286300003807
PB - SciTePress