Systematic Method for the Energy-Saving Potential Calculation of Air Conditioning Systems via Data Mining. Part II: A Detailed Case Study

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-savin...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) 2021-01, Vol.14 (1), p.86
Main Authors: Ma, Rongjiang, Yang, Shen, Wang, Xianlin, Wang, Xi-Cheng, Shan, Ming, Yu, Nanyang, Yang, Xudong
Format: Article
Language:English
Subjects:
Air conditioning
Algorithms
Case studies
Chillers
Cooling
Data collection
Data mining
Data processing
Datasets
Energy
Energy conservation
Energy consumption
Energy efficiency
Energy management
energy-saving potential
Feasibility studies
HVAC
Methods
Neural networks
operational data
Optimization
recognition
Regression analysis
Water shortages
Water supply
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process.
ISSN:1996-1073
1996-1073
DOI:10.3390/en14010086