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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 6335

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Special Issue Editors

Dr. Kyungmo Kang

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Guest Editor

Division of Architectural Engineering, Daejin University, 1007 Hoguk-ro, Pocheon-si 11159, Republic of Korea
Interests: indoor air quality; building energy; indoor environment

Dr. Daeung Kim

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Guest Editor

Department of Architectural Engineering, Cheongju University, 298 Daesung-ro, Cheongju 28503, Republic of Korea
Interests: building energy efficiency; ventilation performance; IAQ; NZEB
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Special Issue Information

Dear Colleagues,

The issue of carbon neutrality is gaining more importance as global warming continues, pushing governments, industries, and societies to adopt more sustainable practices. The building sector is one of the major energy-consuming industries and has a significant role in achieving carbon neutrality. Furthermore, the demand for a better-built environment has been rising, driven by many recent technological advancements. The growing importance of carbon neutrality has led to considerable research being carried out on improving energy efficiency in the building sector.

The demand for a better-built environment has increased in recent years, fueled by technological advancements in construction, materials, and energy systems. These developments not only aim to reduce energy consumption but also to create healthier, more comfortable indoor environments.

The aim of this Special Issue is to find a new research area on building energy-saving and indoor and built environments. It seeks to address the energy challenges faced by the building sector and explore potential pathways toward carbon-neutral buildings. The main topics of interests include the following:

Building energy;
Indoor air quality;
Indoor environmental quality;
Thermal comfort;
Advanced building control and optimization;
Building simulation;
Building materials;
IoT technology.

Dr. Kyungmo Kang
Dr. Daeung Kim
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

building energy
indoor air quality
indoor environmental quality
thermal comfort
advanced building control and optimization
building simulation
building materials
IoT technology
building information modeling

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Published Papers (6 papers)

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Research

28 pages, 6628 KB

Open AccessArticle

Unified AI Framework for Decarbonization in Large-Scale Building Energy Systems: Integrating Acoustic-Vision Leak Detection and Schedule-Aware Machine Learning

by Mooyoung Yoo

Buildings 2026, 16(9), 1698; https://doi.org/10.3390/buildings16091698 - 26 Apr 2026

Viewed by 312

Abstract

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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14 pages, 1813 KB

Open AccessArticle

Optimization of Multi-Layer Neural Network-Based Cooling Load Prediction for Office Buildings Through Data Preprocessing and Algorithm Variations

by Namchul Seong, Daeung Danny Kim and Goopyo Hong

Buildings 2026, 16(3), 566; https://doi.org/10.3390/buildings16030566 - 29 Jan 2026

Viewed by 369

Abstract

Accurate forecasting of cooling loads is essential for the effective operation of Building Energy Management Systems (BEMSs) and the reduction of building-sector carbon emissions. Although Artificial Neural Networks (ANNs), particularly Multi-Layer Perceptrons (MLPs), have shown strong capability in modeling nonlinear thermal dynamics, their reliability in practice is often limited by inappropriate training algorithm selection and poor data quality, including missing values and numerical distortions. To address these limitations, this study conducts a comprehensive empirical investigation into the effects of training algorithms and systematic data preprocessing strategies on cooling load prediction performance using an MLP model. Through benchmarking ten distinct training algorithms under identical conditions, the Levenberg–Marquardt (LM) algorithm was identified as achieving the lowest prediction error when integrated data preprocessing was applied. In particular, the application of data preprocessing reduced the CvRMSE from 18.56% to 6.03% during the testing period. Furthermore, the proposed framework effectively mitigated zero-load prediction errors during non-cooling periods and improved prediction accuracy under high-load operating conditions. These results provide practical and quantitative guidance for developing robust data-driven forecasting models applicable to real-time building energy optimization. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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24 pages, 3021 KB

Open AccessArticle

Simulation-Based Fault Detection and Diagnosis for AHU Systems Using a Deep Belief Network

by Mooyoung Yoo

Buildings 2026, 16(2), 342; https://doi.org/10.3390/buildings16020342 - 14 Jan 2026

Cited by 1 | Viewed by 750

Abstract

Heating, ventilation, and air conditioning (HVAC) systems account for a significant portion of building energy consumption and play a crucial role in maintaining indoor comfort. However, hidden faults in air-handling units (AHUs) often lead to energy waste and degraded performance, highlighting the importance of reliable fault detection and diagnosis (FDD). This study proposes a simulation-driven FDD framework that integrates a standardized prototype dataset and an independent evaluation dataset generated from a calibrated EnergyPlus model representing a target facility, enabling controlled experimentation and transfer evaluation within simulation environments. Training data were generated from the DOE EnergyPlus Medium Office prototype model, while evaluation data were obtained from a calibrated building-specific EnergyPlus model of a research facility operated by Company H in Korea. Three representative fault scenarios—outdoor air damper stuck closed, cooling coil fouling (65% capacity), and air filter fouling (30% pressure drop)—were systematically implemented. A Deep Belief Network (DBN) classifier was developed and optimized through a two-stage hyperparameter tuning strategy, resulting in a three-layer architecture (256–128–64 nodes) with dropout and regularization for robustness. The optimized DBN achieved diagnostic accuracies of 92.4% for the damper fault, 98.7% for coil fouling, and 95.9% for filter fouling. These results confirm the effectiveness of combining simulation-based dataset generation with advanced deep learning methods for HVAC fault diagnosis. The results indicate that a DBN trained on a standardized EnergyPlus prototype can transfer to a second, independently calibrated EnergyPlus building model when AHU topology, control logic, and monitored variables are aligned. This study should be interpreted as a simulation-based proof-of-concept, motivating future validation with field BMS data and more diverse fault scenarios. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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20 pages, 1609 KB

Open AccessArticle

Low-Cost Gas Sensing and Machine Learning for Intelligent Refrigeration in the Built Environment

by Mooyoung Yoo

Buildings 2026, 16(1), 41; https://doi.org/10.3390/buildings16010041 - 22 Dec 2025

Viewed by 596

Abstract

Accurate, real-time monitoring of meat freshness is essential for reducing food waste and safeguarding consumer health, yet conventional methods rely on costly, laboratory-grade spectroscopy or destructive analyses. This work presents a low-cost electronic-nose platform that integrates a compact array of metal-oxide gas sensors (Figaro TGS2602, TGS2603, and Sensirion SGP30) with a Gaussian Process Regression (GPR) model to estimate a continuous freshness index under refrigerated storage. The pipeline includes headspace sensing, baseline normalization and smoothing, history-window feature construction, and probabilistic prediction with uncertainty. Using factorial analysis and response-surface optimization, we identify history length and sampling interval as key design variables; longer temporal windows and faster sampling consistently improve accuracy and stability. The optimized configuration (≈143-min history, ≈3-min sampling) reduces mean absolute error from ~0.51 to ~0.05 on the normalized freshness scale and shifts the error distribution within specification limits, with marked gains in process capability and yield. Although it does not match the analytical precision or long-term robustness of spectrometric approaches, the proposed system offers an interpretable and energy-efficient option for short-term, laboratory-scale monitoring under controlled refrigeration conditions. By enabling probabilistic freshness estimation from low-cost sensors, this GPR-driven e-nose demonstrates a proof-of-concept pathway that could, after further validation under realistic cyclic loads and operational disturbances, support more sustainable meat management in future smart refrigeration and cold-chain applications. This study should be regarded as a methodological, laboratory-scale proof-of-concept that does not demonstrate real-world performance or operational deployment. The technical implications described herein are hypothetical and require extensive validation under realistic refrigeration conditions. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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26 pages, 7556 KB

Open AccessArticle

Reduction Characteristics of Stack-Effect Problems According to Applying Local Countermeasures by Pressure Distribution Measurement in Buildings

by Taeyon Hwang, Min-ku Hwang and Joowook Kim

Buildings 2025, 15(24), 4453; https://doi.org/10.3390/buildings15244453 - 10 Dec 2025

Viewed by 1011

Abstract

Stack effects in high-rise buildings cause noise, drafts, and elevator door malfunctions during cold weather yet remain difficult to control. Because vertical shafts couple pressures between floors, local fixes at a single lobby can unintentionally disturb the pressure field elsewhere. To analyze these interactions, we developed a measurement-calibrated CONTAM multizone model of a 43-story office building and evaluated representative local countermeasures. Under base winter conditions, the pressure difference across the problematic first-floor high-rise elevator doors is 56 Pa, driving approximately 1300 CMH of airflow through the door line. First-floor depressurization reduces this to 34 Pa (about 30% lower airflow) but simultaneously increases the pressure at the main entrance doors from 19 to 39 Pa. Additional first-floor partitions slightly reduce pressures on upper high-rise floors, whereas opening exterior windows in the high-rise zone increases shaft airflow by 7.7% and further amplifies elevator door pressures. We show that neutral pressure level (NPL) shifts into vertical shafts are a key mechanism limiting the effectiveness of purely local interventions. These results demonstrate that effective countermeasures must be designed at the whole-building scale, jointly controlling pressure redistribution and neutral-pressure-level movement while directing unavoidable pressure transfer toward the exterior envelope and away from sensitive interior spaces. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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28 pages, 15018 KB

Open AccessArticle

The Influence of Hot and Humid Climate Data on Modern Architectural Façade Design: A Case Study of Educational Buildings in Adana, Türkiye

by Gökhan Uşma and Derya Sökmen Kök

Buildings 2025, 15(11), 1939; https://doi.org/10.3390/buildings15111939 - 3 Jun 2025

Cited by 1 | Viewed by 2500

Abstract

This study focuses on how modern architecture can be interpreted using regional data and how hot and humid climate data ultimately affect façade design. Despite modernism’s dominance in 20th-century architecture, research on its adaptation to climates remains limited. Five educational buildings of modern architectural style in Adana, Türkiye, a city with a hot–humid climate, were selected for detailed analysis. These buildings were evaluated based on key façade parameters such as opening configurations and solar shading elements. Additionally, thermal imaging, sun-path diagram simulation, and thermal comfort evaluations were conducted to assess façade performance. The findings suggest that contrary to criticisms of modern architecture’s disregard for local conditions, the studied buildings integrate climate-responsive design strategies. In contrast to contemporary architecture’s reliance on technical equipment for thermal comfort, this study also demonstrates that passive design strategies and structural decisions can offer effective alternatives in hot and humid climates. Full article

(This article belongs to the Special Issue Built Environment and Building Energy for Decarbonization)

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