Generative pre-trained transformers (GPT)-based automated data mining for building energy management: Advantages, limitations and the future DOI Creative Commons
Chaobo Zhang,

Jie Lu,

Yang Zhao

et al.

Energy and Built Environment, Journal Year: 2023, Volume and Issue: 5(1), P. 143 - 169

Published: June 16, 2023

Advanced data mining methods have shown a promising capacity in building energy management. However, the past decade, such are rarely applied practice, since they highly rely on users to customize solutions according characteristics of target systems. Hence, major barrier is that practical applications remain laborious. It necessary enable computers human-like ability solve tasks. Generative pre-trained transformers (GPT) might be capable addressing this issue, as some GPT models GPT-3.5 and GPT-4 powerful abilities interaction with humans, code generation, inference common sense domain knowledge. This study explores potential most advanced model (GPT-4) three scenarios management, i.e., load prediction, fault diagnosis, anomaly detection. A performance evaluation framework proposed verify capabilities generating prediction codes, diagnosing device faults, detecting abnormal system operation patterns. demonstrated can automatically tasks domain, which overcomes domain. In exploration GPT-4, its advantages limitations also discussed comprehensively for revealing future research directions

Language: Английский

Fault detection and diagnosis of large-scale HVAC systems in buildings using data-driven methods: A comprehensive review DOI

Maryam Sadat Mirnaghi,

Fariborz Haghighat

Energy and Buildings, Journal Year: 2020, Volume and Issue: 229, P. 110492 - 110492

Published: Sept. 23, 2020

Language: Английский

Citations

289
AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives DOI Creative Commons
Yassine Himeur, Mariam Elnour, Fodil Fadli

et al.

Artificial Intelligence Review, Journal Year: 2022, Volume and Issue: 56(6), P. 4929 - 5021

Published: Oct. 15, 2022

In theory, building automation and management systems (BAMSs) can provide all the components functionalities required for analyzing operating buildings. However, in reality, these only ensure control of heating ventilation air conditioning system systems. Therefore, many other tasks are left to operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying changes needed improve efficiency, ensuring security privacy end-users, etc. To that end, there has been a movement developing artificial intelligence (AI) big data analytic tools as they offer various new tailor-made solutions incredibly appropriate practical management. Typically, help operator (i) tons connected equipment data; and; (ii) making intelligent, efficient, on-time decisions performance. This paper presents comprehensive systematic survey on using AI-big analytics BAMSs. It covers AI-based tasks, load forecasting, water management, indoor environmental quality monitoring, occupancy detection, The first part this adopts well-designed taxonomy overview existing frameworks. A review is conducted about different aspects, including learning process, environment, computing platforms, application scenario. Moving on, critical discussion performed identify current challenges. second aims at providing reader with insights into real-world analytics. Thus, three case studies demonstrate use BAMSs presented, focusing anomaly detection residential office buildings performance optimization sports facilities. Lastly, future directions valuable recommendations identified reliability intelligent

Language: Английский

Citations

274
Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System DOI
Prabhakar Sharma, Zafar Said,

Anurag Kumar

et al.

Energy & Fuels, Journal Year: 2022, Volume and Issue: 36(13), P. 6626 - 6658

Published: June 13, 2022

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity working fluid has a huge impact on efficiency system. addition small amount high thermal conductivity solid nanoparticles to base improves transfer. Even though large research data is available literature, some results are contradictory. Many influencing factors, as well nonlinearity refutations, make nanofluid highly challenging obstruct its potentially valuable uses. On other hand, data-driven machine learning techniques would be very useful for forecasting thermophysical features rate, identifying most influential assessing efficiencies different primary aim this review study look at applications employed nanofluid-based system, reveal new developments research. A variety modern algorithms studies systems examined, along with their advantages disadvantages. Artificial neural networks-based model prediction using contemporary commercial software simple develop popular. prognostic may further improved by combining marine predator algorithm, genetic swarm intelligence optimization, intelligent optimization approaches. In well-known networks fuzzy- gene-based techniques, newer ensemble such Boosted regression K-means, K-nearest neighbor (KNN), CatBoost, XGBoost gaining due architectures adaptabilities diverse types. regularly used fuzzy-based mostly black-box methods, user having little or no understanding how they function. This reason concern, ethical artificial required.

Language: Английский

Citations

245
Review of urban building energy modeling (UBEM) approaches, methods and tools using qualitative and quantitative analysis DOI Creative Commons
Usman Ali, Mohammad Haris Shamsi, Cathal Hoare

et al.

Energy and Buildings, Journal Year: 2021, Volume and Issue: 246, P. 111073 - 111073

Published: May 25, 2021

The world has witnessed a significant population shift to urban areas over the past few decades. Urban account for about two-thirds of world's total primary energy consumption, which building sector constitutes proportion approximately 40%. Stakeholders such as planners and policy makers face substantial challenges when targeting sustainable climate goals related buildings' sector, i.e. reduce use associated emissions. modeling is one possible solution that leverages limited resources estimate support appropriate formation. Over years, there have been only review studies on approaches. These lack an in-depth discussion future research opportunities data-driven, reduced-order, simulation-based methods. This paper proposes Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis approaches, methods tools used modeling. Furthermore, this generalized framework based existing literature different aim study assist policymakers choosing develop implement planning projects available resources.

Language: Английский

Citations

196
An overview of machine learning applications for smart buildings DOI Creative Commons
Kari Alanne, Seppo Sierla

Sustainable Cities and Society, Journal Year: 2021, Volume and Issue: 76, P. 103445 - 103445

Published: Oct. 13, 2021

The efficiency, flexibility, and resilience of building-integrated energy systems are challenged by unpredicted changes in operational environments due to climate change its consequences. On the other hand, rapid evolution artificial intelligence (AI) machine learning (ML) has equipped buildings with an ability learn. A lot research been dedicated specific applications for phases a building's life-cycle. reviews commonly take specific, technological perspective without vision integration smart technologies at level whole system. Especially, there is lack discussion on roles autonomous AI agents training boosting process complex abruptly changing environments. This review article discusses system-level presents overview that make independent decisions building management. We conclude buildings’ adaptability can be enhanced system through AI-initiated processes using digital twins as greatest potential efficiency improvement achieved integrating solutions timescales HVAC control electricity market participation.

Language: Английский

Citations

193
Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches DOI Open Access
Cheng Fan, Da Yan, Fu Xiao

et al.

Building Simulation, Journal Year: 2020, Volume and Issue: 14(1), P. 3 - 24

Published: Oct. 23, 2020

Language: Английский

Citations

163
Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification DOI
Zhenxiang Dong, Jiangyan Liu, Bin Liu

et al.

Energy and Buildings, Journal Year: 2021, Volume and Issue: 241, P. 110929 - 110929

Published: March 20, 2021

Language: Английский

Citations

161
Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies DOI Creative Commons
Yiqun Pan,

Mingya Zhu,

Yan Lv

et al.

Advances in Applied Energy, Journal Year: 2023, Volume and Issue: 10, P. 100135 - 100135

Published: April 6, 2023

As one of the most important and advanced technology for carbon-mitigation in building sector, performance simulation (BPS) has played an increasingly role with powerful support energy modelling (BEM) energy-efficient designs, operations, retrofitting buildings. Owing to its deep integration multi-disciplinary approaches, researchers, as well tool developers practitioners, are facing opportunities challenges during application BEM at multiple scales stages, e.g., building/system/community levels planning/design/operation stages. By reviewing recent studies, this paper aims provide a clear picture how performs solving different research questions on varied phase spatial resolution, focus objectives frameworks, methods tools, applicability transferability. To guide future applications performance-driven management, we classified current trends into five topics that span through stages levels: (1) Simulation design new retrofit design, (2) Model-based operational optimization, (3) Integrated using data measurements digital twin, (4) Building supporting urban planning, (5) Modelling building-to-grid interaction demand response. Additionally, recommendations discussed, covering potential occupancy behaviour modelling, machine learning, quantification model uncertainties, linking monitoring systems.

Language: Английский

Citations

143
Building energy consumption prediction using multilayer perceptron neural network-assisted models; comparison of different optimization algorithms DOI
Sadegh Afzal,

Behrooz M. Ziapour,

Afshar Shokri

et al.

Energy, Journal Year: 2023, Volume and Issue: 282, P. 128446 - 128446

Published: July 15, 2023

Language: Английский

Citations

114
Hybrid physics-based and data-driven models for smart manufacturing: Modelling, simulation, and explainability DOI
Jinjiang Wang, Yilin Li, Robert X. Gao

et al.

Journal of Manufacturing Systems, Journal Year: 2022, Volume and Issue: 63, P. 381 - 391

Published: April 1, 2022

Language: Английский

Citations

101