An Adaptive Control Model for Thermal Environmental Factors to Supplement the Sustainability of a Small-Sized Factory DOI Open Access
Jonghoon Ahn

Sustainability, Journal Year: 2023, Volume and Issue: 15(24), P. 16619 - 16619

Published: Dec. 6, 2023

Effective indoor thermal controls can have quantifiable advantages of improving energy efficiency and environmental quality, which also lead to additional benefits such as better workability, productivity, economy in buildings. However, the case factory buildings whose main usage is produce process goods, securing comfort for their workers has been regarded a secondary problem. This study aims explore method cooling heating air supply improve by use data-driven adaptive model. The genetic algorithm using idea occupancy rate helps model effectively analyze environment determine optimized conditions comfort. As result, proposed successfully shows performance, confirms that there 2.81% saving consumption 16–32% reduction dissatisfaction. In particular, significance this dissatisfaction be reduced simultaneously despite precise air-supply are performed response building, weather, rate.

Language: Английский

A review on enhancing energy efficiency and adaptability through system integration for smart buildings DOI

Um-e-Habiba,

Ijaz Ahmed, Mohammad Asif

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 89, P. 109354 - 109354

Published: April 18, 2024

Language: Английский

Citations

32
Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions DOI Creative Commons
Panagiotis Michailidis, Iakovos Michailidis, Socratis Gkelios

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 570 - 570

Published: Jan. 24, 2024

ANNs have become a cornerstone in efficiently managing building energy management systems (BEMSs) as they offer advanced capabilities for prediction, control, and optimization. This paper offers detailed review of recent, significant research this domain, highlighting the use optimizing key systems, such HVAC domestic water heating (DHW) lighting (LSs), renewable sources (RESs), which been integrated into environment. After illustrating conceptual background most common ANN architectures controlling BEMSs, current work dives deep relative applications, thereby exhibiting their methodology outcomes. By summarizing numerous impactful applications during 2015–2023, categorizes predominant ANN-based techniques according to methodological approach, specific equipment, experimental setups. Grounded different perspectives that studies illustrate, primary focus is evaluate overall status ANN-driven control management, well understanding prevailing trends at level. Leveraging graphical depictions comparisons between concepts, future directions, fruitful conclusions are drawn, upcoming innovations frameworks BEMSs highlighted.

Language: Английский

Citations

9
Dynamic Personalized Thermal Comfort Model:Integrating Temporal Dynamics and Environmental Variability with Individual Preferences DOI Creative Commons
Abdulrazaq AbdulRaheem, Seungho Lee, Im Y. Jung

et al.

Journal of Building Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111938 - 111938

Published: Jan. 1, 2025

Language: Английский

Citations

1
Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis DOI Creative Commons
Jingyi Liu, J.F. Chen

Buildings, Journal Year: 2025, Volume and Issue: 15(7), P. 994 - 994

Published: March 21, 2025

With the rapid advancement of machine learning (ML) technologies, their innovative applications in enhancing building energy efficiency are increasingly prominent. Utilizing tools such as VOSviewer and Bibliometrix, this study systematically reviews body related literature, focusing on key emerging trends cutting-edge ML techniques, including deep learning, reinforcement unsupervised optimizing performance managing carbon emissions. First, paper delves into role prediction, intelligent management, sustainable design, with particular emphasis how smart systems leverage real-time data analysis prediction to optimize usage significantly reduce emissions dynamically. Second, summarizes technological evolution future sector identifies critical challenges faced by field. The findings provide a technology-driven perspective for advancing sustainability construction industry offer valuable insights research directions.

Language: Английский

Citations

1
Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management DOI Creative Commons
Charalampos Rafail Lazaridis, Iakovos Michailidis, Georgios D. Karatzinis

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 581 - 581

Published: Jan. 25, 2024

The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch machine where algorithms learn by interacting with the environment—has emerged as prominent solution to this challenge. However, modern literature exhibits plethora RL methodologies, rendering selection most suitable one significant This work focuses on evaluating various methodologies saving adequate levels residential setting. Five algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Gradient (DDPG), Q-Network (DQN), Advantage Actor-Critic (A2C), Soft (SAC)—are being thoroughly compared towards baseline conventional control approach, exhibiting their potential improve use ensuring comfortable living environment. integrated comparison between different emphasizes subtle strengths weaknesses each algorithm, indicating that best relies heavily particular objectives.

Language: Английский

Citations

5
Prospects and Challenges of Reinforcement Learning- Based HVAC Control DOI

Ajifowowe Iyanu,

Hojong Chang,

C Lee

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 111080 - 111080

Published: Oct. 1, 2024

Language: Английский

Citations

5
Thermodynamic Optimization of Building HVAC Systems Through Dynamic Modeling and Advanced Machine Learning DOI Open Access
Samuel Moveh,

Emmanuel Alejandro Merchán-Cruz,

Ahmed Ibrahim

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(5), P. 1955 - 1955

Published: Feb. 25, 2025

This study enhances thermodynamic efficiency and demand response in an office building’s HVAC system using machine learning (ML) model predictive control (MPC). study, conducted a simulated EnergyPlus 8.9 environment integrated with MATLAB (R2023a, 9.14), focuses on optimizing the of building Jeddah, Kingdom Saudi Arabia. Support vector regression (SVR) deep reinforcement (DRL) were selected for their accuracy adaptability dynamic environments, exergy destruction analysis used to assess efficiency. The models, MPC, aimed reduce improve response. Simulations evaluated room temperature prediction, energy optimization, cost reduction. DRL showed superior prediction accuracy, reducing costs by 21.75% while keeping indoor increase minimal at 0.12 K. simulation-based approach demonstrates potential combining ML MPC optimize use support programs effectively.

Language: Английский

Citations

0
HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests DOI Creative Commons
Diana D’Agostino, Federico Minelli, Francesco Minichiello

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(6), P. 1526 - 1526

Published: March 19, 2025

The operation of Heating Ventilation and Air Conditioning (HVAC) systems in densely occupied spaces results considerable energy consumption. In the post-pandemic context, stricter indoor air quality standards higher ventilation rates further increase demand. this paper, retrofit a partial recirculation all-air HVAC system serving university lecture room located Southern Italy is analyzed. Multi-Objective Optimization (MOO) Multi-Criteria Decision-Making (MCDM) approaches are used to find optimal design alternatives rank these considering two different decision-makers, i.e., public private stakeholders. Among Pareto solutions obtained from optimization, alternative identified, encompassing three Key Performance Indicators using new robust MCDM approach based on four methods, TOPSIS, VIKOR, WASPAS, MULTIMOORA. show that, era, baseline scenarios for infection reduction that do not involve introduction demand control strategies cause consumption negligible values up 59%. On contrary, involving decrease between 5% 38%. findings offer valuable guidance retrofits education similar buildings, emphasizing potential balance occupant health, efficiency, cost reduction. also highlight significant CO2 reductions minimal impacts thermal comfort, showcasing substantial savings through targeted retrofits.

Language: Английский

Citations

0
Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations DOI Creative Commons
Panagiotis Michailidis, Iakovos Michailidis, Elias B. Kosmatopoulos

et al.

Energies, Journal Year: 2025, Volume and Issue: 18(7), P. 1724 - 1724

Published: March 30, 2025

The integration of renewable energy systems into modern buildings is essential for enhancing efficiency, reducing carbon footprints, and advancing intelligent management. However, optimizing RES operations within building management introduces significant complexity, requiring advanced control strategies. One branch algorithms concerns reinforcement learning, a data-driven strategy capable dynamically managing sources other subsystems under uncertainty real-time constraints. current review systematically examines RL-based strategies applied in BEMS frameworks integrating technologies between 2015 2025, classifying them by algorithmic approach evaluating the role multi-agent hybrid methods improving adaptability occupant comfort. Following thorough explanation rigorous selection process—which targeted most impactful peer-reviewed publications from last decade, paper presents mathematical concepts RL RL, along with detailed summaries summary tables integrated works to facilitate quick reference key findings. For evaluation, outlines different attributes field considering following: methodologies RL; agent types; value-action networks; reward functions; baseline approaches; typologies. Grounded on findings presented evaluation section, offers structured synthesis emerging research trends future directions, identifying strengths limitations

Language: Английский

Citations

0
Methodological Advances in Temperature Dynamics Modeling for Energy-Efficient Indoor Air Management Systems DOI Creative Commons

F.C. Iglesias,

Joaquim Massana, Llorenç Burgas

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(8), P. 4291 - 4291

Published: April 13, 2025

Heating, ventilation, and air conditioning (HVAC) systems account for up to 40% of the total energy consumption in buildings. Improving modeling HVAC components is necessary optimize efficiency, maintain indoor thermal comfort, reduce their carbon footprint. This work addresses lack a general methodology data preprocessing by introducing novel approach feature extraction selection based on physical equations expert knowledge that can be applied any data-driven model. The proposed framework enables forecasting temperatures individual components. validated with real-world from system involving fan coil unit inertia deposit powered geothermal energy, achieving coefficient determination (R2) 0.98 mean absolute percentage error (MAPE) 0.44%.

Language: Английский

Citations

0