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

Journal of Building Engineering, Год журнала: 2024, Номер 89, С. 109354 - 109354

Опубликована: Апрель 18, 2024

Язык: Английский

---

Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions

Energies, Год журнала: 2024, Номер 17(3), С. 570 - 570

Опубликована: Янв. 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.

Язык: Английский

---

Dynamic Personalized Thermal Comfort Model:Integrating Temporal Dynamics and Environmental Variability with Individual Preferences

Journal of Building Engineering, Год журнала: 2025, Номер unknown, С. 111938 - 111938

Опубликована: Янв. 1, 2025

Язык: Английский

---

HVAC System Energy Retrofit for a University Lecture Room Considering Private and Public Interests

Energies, Год журнала: 2025, Номер 18(6), С. 1526 - 1526

Опубликована: Март 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.

Язык: Английский

---

Applications and Trends of Machine Learning in Building Energy Optimization: A Bibliometric Analysis

Buildings, Год журнала: 2025, Номер 15(7), С. 994 - 994

Опубликована: Март 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.

Язык: Английский

---

Reinforcement Learning for Optimizing Renewable Energy Utilization in Buildings: A Review on Applications and Innovations

Energies, Год журнала: 2025, Номер 18(7), С. 1724 - 1724

Опубликована: Март 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

Язык: Английский

---

Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management

Energies, Год журнала: 2024, Номер 17(3), С. 581 - 581

Опубликована: Янв. 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.

Язык: Английский

---

Prospects and Challenges of Reinforcement Learning- Based HVAC Control

Journal of Building Engineering, Год журнала: 2024, Номер unknown, С. 111080 - 111080

Опубликована: Окт. 1, 2024

Язык: Английский

---

Thermodynamic Optimization of Building HVAC Systems Through Dynamic Modeling and Advanced Machine Learning

Sustainability, Год журнала: 2025, Номер 17(5), С. 1955 - 1955

Опубликована: Фев. 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.

Язык: Английский

---

Collaborative Optimization Scheduling Strategy for HVAC with a Three-Layer Optimization Architecture

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115565 - 115565

Опубликована: Март 1, 2025

Язык: Английский

---