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

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

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Modelling occupant behaviour in residential buildings: A systematic literature review

Building and Environment, Journal Year: 2024, Volume and Issue: 265, P. 111959 - 111959

Published: Aug. 14, 2024

Language: Английский

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

Energies, Journal Year: 2024, Volume and Issue: 17(19), P. 4988 - 4988

Published: Oct. 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.

Language: Английский

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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, Journal Year: 2025, Volume and Issue: unknown, P. 115436 - 115436

Published: Feb. 1, 2025

Language: Английский

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Transfer learning for smart construction: Advances and future directions

Automation in Construction, Journal Year: 2025, Volume and Issue: 175, P. 106238 - 106238

Published: May 1, 2025

Language: Английский

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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, Journal Year: 2025, Volume and Issue: unknown, P. 113045 - 113045

Published: May 1, 2025

Language: Английский

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

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(3), P. 1618 - 1618

Published: Feb. 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.

Language: Английский

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

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115721 - 115721

Published: April 1, 2025

Language: Английский

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

Building and Environment, Journal Year: 2024, Volume and Issue: 263, P. 111882 - 111882

Published: July 27, 2024

Language: Английский

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Innovative Energy Solutions: Evaluating Reinforcement Learning Algorithms for Battery Storage Optimization in Residential Settings

Process Safety and Environmental Protection, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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