Modelling occupant behaviour in residential buildings: A systematic literature review

Building and Environment, Год журнала: 2024, Номер 265, С. 111959 - 111959

Опубликована: Авг. 14, 2024

Язык: Английский

---

Enhancing Energy Systems and Rural Communities through a System of Systems Approach: A Comprehensive Review

Energies, Год журнала: 2024, Номер 17(19), С. 4988 - 4988

Опубликована: Окт. 6, 2024

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different sources technologies. In this paper, we explore the system of (SoS) approach, which provides a comprehensive framework for improving systems’ interoperability, efficiency, resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, storage, renewable energy, smart cities, rural communities, study highlights essential role SoSs addressing challenges transition. The principal areas interest include integration advanced control algorithms machine learning techniques development robust communication networks manage interactions between interconnected subsystems. This also identifies significant associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, need harmonized regulatory frameworks. outlines future directions intelligence autonomy subsystems, are achieving sustainable, resilient, adaptive infrastructure.

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

---

Total Cost of Ownership Prediction in Chilled Water Plants: Contributing Factors and Role of Artificial Intelligence

Applied Sciences, Год журнала: 2025, Номер 15(3), С. 1618 - 1618

Опубликована: Фев. 5, 2025

This study investigates key parameters and applications of artificial intelligence (AI) in predicting the total cost ownership (TCO) for chilled water plants (CWPs). Forecasting TCO CWPs is challenging due to diverse dynamic factors that influence it, necessitating understanding their complex correlations causations. While AI non-AI approaches have improved parameter prediction accuracy different engineering applications, comprehensive literature reviews on chiller methodologies influencing are limited. systematic review addresses three objectives: (1) identify estimating CWPs, (2) examine existing techniques employed forecasting benefits energy savings, (3) evaluate how enhances robustness. Following preferred reporting items meta-analyses (PRISMA) guidelines, this analyzed studies from 2017 2024 sourced Web Science Scopus databases. identifies several TCO, including cooling load, consumption, capacity, Coefficient Performance (COP). The shows AI-driven models, such as deep learning machine algorithms, robustness predictions, it further demonstrates scenarios where outperforms conventional methods. Notably, current predicted be capable reducing life cycle costs by up 18%, based modeling estimates.

Язык: Английский

---

Data-driven Pre-training Framework for Reinforcement Learning of Air-Source Heat Pump (ASHP) Systems Based on Historical Data in Office Buildings: Field Validation

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115436 - 115436

Опубликована: Фев. 1, 2025

Язык: Английский

---

Towards Sustainable Energy Use: Reinforcement Learning for Demand Response in Commercial Buildings

Energy and Buildings, Год журнала: 2025, Номер unknown, С. 115721 - 115721

Опубликована: Апрель 1, 2025

Язык: Английский

---

Transfer learning for smart construction: Advances and future directions

Automation in Construction, Год журнала: 2025, Номер 175, С. 106238 - 106238

Опубликована: Май 1, 2025

Язык: Английский

---

Review of Optimization Control Methods for HVAC Systems in Demand Response (DR): Transition from Model-driven to Model-free Approaches and Challenges

Building and Environment, Год журнала: 2025, Номер unknown, С. 113045 - 113045

Опубликована: Май 1, 2025

Язык: Английский

---

Towards Extensive Definition and Planning of Energy Resilience in Buildings in Cold Climate

Buildings, Год журнала: 2024, Номер 14(5), С. 1453 - 1453

Опубликована: Май 17, 2024

The transition towards a sustainable future requires the reliable performance of building’s energy system in order for building to be energy-resilient. “Energy resilient cold climates” is an emerging concept that defines ability maintain minimum level indoor air temperature and minimize occupant’s health risk during disruptive event grid’s power supply loss climate. aim introduce extensive definition resilience buildings apply it case studies. This article first reviews progress provides overview energy-resilient concept. review shows most relevant focus on short-term resilience, serious gap related long-term context regions. presents basic buildings, systematic framework, indicators analyzing buildings. Terms such as active passive habitability, survivability, adaptive habitable conditions are defined. applied two simulated Finnish studies, old new building. By analysis, using defined thresholds, calculated compared. Depending type building, results show robustness period 11 h 26 respectively. failed provide habitability conditions. impact 8.9 °C, (Pmin) 12.54 degree disruption (DoD) 0.300 speed collapse (SoC) 3.75 °C/h, recovery (SoR) 0.64 °C/h. On other hand, performed better 4 Pmin 17.5 DoD 0.138. SoC slow 3.2 °C/h SoR fast 0.80 pathway improvements resilience. In conclusion, this work supports society policy-makers build society.

Язык: Английский

---

Multi-objectives occupant-centric control of thermostats and natural ventilation systems in cold climate conditions using real-time occupant-related information

Building and Environment, Год журнала: 2024, Номер 263, С. 111882 - 111882

Опубликована: Июль 27, 2024

Язык: Английский

---