Hybrid TRRM–deep learning approach for water-cooled centrifugal chiller COP prediction

Authors

Young Il Kim (ORCID: https://orcid.org/0000-0002-1199-267X)
Seong-Hyoung Kim (ORCID: https://orcid.org/0009-0009-1890-6162)

Institutions

Seoul National University of Science and Technology (KR)

Publication Details

Journal: Case Studies in Thermal Engineering
Published: 2025-10-25
DOI: https://doi.org/10.1016/j.csite.2025.107309
Citations: 1
Primary Topic: Building Energy and Comfort Optimization
Type: article
Field-Weighted Citation Impact: 1.01

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article

Hybrid TRRM–deep learning approach for water-cooled centrifugal chiller COP prediction

Young Il Kim, Seong-Hyoung Kim

Oct 25, 2025

1 citations

Case Studies in Thermal Engineering

Building Energy and Comfort Optimization

1.01

article

Hybrid TRRM–deep learning approach for water-cooled centrifugal chiller COP prediction

Young Il Kim, Seong-Hyoung Kim

October 25, 2025article en

1 citations

Abstract

No abstract available for this paper.

Case Studies in Thermal EngineeringVol. 76

Seoul National University of Science and Technology (KR)

Openalex Percentile: Top 19%

Building Energy and Comfort Optimization

1.01

DOI Open Access PDF OpenAlex

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Hybrid TRRM–deep learning approach for water-cooled centrifugal chiller COP prediction — Young Il Kim, Seong-Hyoung Kim · Case Studies in Thermal Engineering (2025) | TGRS Research Map | TGRS