Study on energy consumption prediction of air conditioning system in public buildings based on PSO-ELMAN

Lan Yu; Huanhuan Zhao; Caiwei Yang; Guanghao You; Zhenguo Jia

doi:10.1117/12.3008200

25 October 2023 Study on energy consumption prediction of air conditioning system in public buildings based on PSO-ELMAN

Lan Yu, Huanhuan Zhao, Caiwei Yang, Guanghao You, Zhenguo Jia

Author Affiliations +

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280142 (2023) https://doi.org/10.1117/12.3008200
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

The most important source of energy consumption in public buildings is the energy use of air conditioning system, which is characterized by multiple working conditions, multiple parameters and multiple disturbances. In order to accurately reflect the energy consumption characteristics of air conditioning systems in public buildings, this paper uses the PSO particle swarm algorithm to optimize the ELMAN neural network to establish the prediction model of air conditioning systems in public buildings. The results of the case analysis show that the prediction accuracy of the PSO-ELMAN neural network prediction model is significantly improved compared with the ELMAN neural network, which is a reference value for realizing the research of energy consumption prediction of air conditioning system in public buildings.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lan Yu, Huanhuan Zhao, Caiwei Yang, Guanghao You, and Zhenguo Jia "Study on energy consumption prediction of air conditioning system in public buildings based on PSO-ELMAN", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280142 (25 October 2023); https://doi.org/10.1117/12.3008200

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available