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

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energy and Built Environment 2024年第1期5卷 143-169页

作者： Chaobo Zhang Jie Lu Yang Zhao Institute of Refrigeration and Cryogenics Zhejiang UniversityHangzhouChina Department of the Built Environment Eindhoven University of TechnologyEindhovenThe Netherlands Key Laboratory of Clean energy and Carbon Neutrality of Zhejiang Province Jiaxing Research InstituteZhejiang UniversityJiaxingChina

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

关键词： ChatGPT GPT-4 Artificial general intelligence Data mining building energy management

Machine learning in building energy management: A critical review and future directions

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Frontiers of Engineering management 2022年第2期9卷 239-256页

作者： Qian SHI Chenyu LIU Chao XIAO School of Economics and management Tongji UniversityShanghai 200092China

Over the past two decades,machine learning(ML)has elicited increasing attention in building energy management(BEM)***,the boundary of the ML-BEM research has not been clearly defined,and no thorough review of ML applications in BEM during the whole building life-cycle has been *** study aims to address this gap by reviewing the ML-BEM papers to ascertain the status of this research area and identify future research *** integrated framework of ML-BEM,composed of four layers and a series of driving factors,is ***,based on the hype cycle model,this paper analyzes the current development status of ML-BEM and tries to predict its future development ***,five research directions are discussed:(1)the behavioral impact on BEM,(2)the integration management of renewable energy,(3)security concerns of ML-BEM,(4)extension to other building life-cycle phases,and(5)the focus on fault detection and *** findings of this study are believed to provide useful references for future research on ML-BEM.

关键词： building energy management machine learning integrated framework knowledge evolution

energy Optimization for building energy management with Thermal Storage: A Multi-Agent Deep Reinforcement Learning Approach

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Energy Optimization for Building Energy Management with Ther...

Reinforcement Learning Model for energy System management to Ensure energy Efficiency and Comfort in buildings

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作者： Shifeng Zhu Wei Lu Yi Feng Changhai Sun School of Control Science and Engineering Dalian University of Technology College of Electrical Engineering Dalian University of Technology

building energy management(BEM) with Thermal energy Storage(TES) poses significant challenges due to the intricate coordination required among components such as Power-to-Heat(P2H) converters, TES units, and zone temperature controllers. In this paper, we propose a novel multi-agent Deep Reinforcement Learning(DRL) method for BEM, capable of optimizing the energy efficiency and flexibility of building energy Systems(BESs) integrated with TES without requiring manual efforts to set up a control-oriented model. Our method–distributed optimized NNPC via DRL–consists of two parts: i) a Neural Network Predictive Control(NNPC) for zone temperature regulation, leveraging an attention-based artificial neural network for forecasting operational conditions and a Dynamic Programming(DP) algorithm to jointly optimize both comfort levels and energy efficiency;and ii) a distributed optimization strategy realized through a coordinating policy that facilitates the collaboration between conventional on/off and NNPC agents/controllers. The efficacy of our method is confirmed through simulations established based on a real-world BES that integrates a TES unit. We compare our method with three alternatives: a conventional on/off control, an optimized on/off control, and an optimized Na?ve NNPC, with the latter two also incorporating our distributed optimization strategy. Specifically, our method realizes approximately 7% reduction in energy consumption compared to the conventional on-off method while maintaining equivalent comfort levels. Furthermore, it attains parity with the performance of the optimized Na?ve NNPC without requiring manual tuning, thereby illustrating its adaptability and ease of implementation. Collectively, these findings underscore our method's superiority across metrics of energy efficiency, comfort and applicability, demonstrating the profound potential of DRL in BEM, particularly when TES is involved.

关键词： building energy management Thermal energy Storage Operational Optimization Deep Reinforcement Learning Neural Network Predictive Control

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energy Engineering 2024年第12期121卷 3617-3634页

作者： Inna Bilous Dmytro Biriukov Dmytro Karpenko Tatiana Eutukhova Oleksandr Novoseltsev Volodymyr Voloshchuk Department of Heat and Alternative Power Engineering National Technical University of Ukraine“KPI”Kyiv03056Ukraine Department of Structural Transformation of the Fuel and energy Complex General Energy Institute of NAS of UkraineKyiv03150Ukraine

This article focuses on the challenges ofmodeling energy supply systems for buildings,encompassing both methods and tools for simulating thermal regimes and engineering systems within *** the comfort of living or working in buildings often necessitates increased consumption of energy and material,such as for thermal upgrades,which consequently incurs additional economic *** is crucial to acknowledge that such improvements do not always lead to a decrease in total pollutant emissions,considering emissions across all stages of production and usage of energy and materials aimed at boosting energy efficiency and comfort in *** addition,it explores the methods and mechanisms for modeling the operating modes of electric boilers used to collectively improve energy efficiency and indoor climatic *** the developed mathematical models,the study examines the dynamic states of building energy supply systems and provides recommendations for improving their *** dynamic models are executed in software environments such as MATLAB/Simscape and Python,where the component detailing schemes for various types of controllers are ***,controllers based on reinforcement learning(RL)displayed more adaptive load level *** RL-based controllers can lower instantaneous power usage by up to 35%,reduce absolute deviations from a comfortable temperature nearly by half,and cut down energy consumption by approximately 1%while maintaining *** the energy source produces a constant energy amount,the RL-based heat controllermore effectively maintains the temperature within the set range,preventing *** conclusion,the introduced energydynamic building model and its software implementation offer a versatile tool for researchers,enabling the simulation of various energy supply systems to achieve optimal energy efficiency and indoor climate control in buildings.

关键词： building energy management building heating system dynamic modeling reinforcement learning energy efficiency comfortable temperature

An innovative heterogeneous transfer learning framework to enhance the scalability of deep reinforcement learning controllers in buildings with integrated energy systems

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building Simulation 2024年第5期17卷 739-770页

作者： Davide Coraci Silvio Brandi Tianzhen Hong Alfonso Capozzoli Politecnico di Torino Department of EnergyTEBE research groupBAEDA Lab.Corso Duca degli Abruzzi 24Torino10129Italy building Technology and Urban Systems Division Lawrence Berkeley National LaboratoryOne Cyclotron RoadBerkeleyCA 94720USA

Deep Reinforcement Learning(DRL)-based control shows enhanced performance in the management of integrated energy systems when compared with Rule-Based Controllers(RBCs),but it still lacks scalability and generalisation due to the necessity of using tailored models for the training *** Learning(TL)is a potential solution to address this ***,existing TL applications in building control have been mostly tested among buildings with similar features,not addressing the need to scale up advanced control in real-world scenarios with diverse energy *** paper assesses the performance of an online heterogeneous TL strategy,comparing it with RBC and offline and online DRL controllers in a simulation setup using energyPlus and *** study tests the transfer in both transductive and inductive settings of a DRL policy designed to manage a chiller coupled with a Thermal energy Storage(TES).The control policy is pre-trained on a source building and transferred to various target buildings characterised by an integrated energy system including photovoltaic and battery energy storage systems,different building envelope features,occupancy schedule and boundary conditions(e.g.,weather and price signal).The TL approach incorporates model slicing,imitation learning and fine-tuning to handle diverse state spaces and reward functions between source and target *** show that the proposed methodology leads to a reduction of 10% in electricity cost and between 10% and 40% in the mean value of the daily average temperature violation rate compared to RBC and online DRL ***,online TL maximises self-sufficiency and self-consumption by 9% and 11% with respect to ***,online TL achieves worse performance compared to offline DRL in either transductive or inductive ***,offline Deep Reinforcement Learning(DRL)agents should be trained at least for 15 episodes to reach the same level of performance as the online

关键词： transfer learning reinforcement learning building control building energy management

Impact of occupant autonomy on satisfaction and building energy efficiency

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energy and Built Environment 2023年第4期4卷 377-385页

作者： Wipa Loengbudnark Kaveh Khalilpour Gnana Bharathy Alexey Voinov Leena Thomas Center on Persuasive Systems for Wise Adaptive Living Faculty of Engineering&ITUniversity of Technology SydneyNSW 2007Australia School of Architecture Faculty of DesignArchitecture and BuildingUniversity of Technology SydneyNSW 2007Australia Faculty of Engineering Technology University of Twentethe Netherlands

The philosophy of building energy management is going through a paradigm change from traditional,often inefficient,user-controlled systems to one that is centrally automated with the aid of IoT-enabled *** this context,occupants’perceived control and building automation may seem to be in *** inquiry of this study is rooted in a proposition that while building automation and centralized control systems are assumed to provide indoor comfort and conserve energy use,limiting occupants’control over their work environment may result in dissatisfaction,and in turn decrease *** assessing this hypothesis,data from the post-occupancy evaluation survey of a smart building in a university in Australia was used to analyze the relationships between perceived control,satisfaction,and perceived *** structural equation modeling,we have found a positive direct effect of occupants’perceived control on overall satisfaction with their working ***,perceived control exerts an influence on perceived productivity through ***,a field experiment conducted in the same building revealed the potential impact that occupant controllability can have on energy *** changed the default light settings from automatic on-and-offto manual-on and automatic-off,letting occupants choose themselves whether to switch the light on or ***,about half of the participants usually kept the lights off,preferring daylight in their *** also resulted in a reduction in lighting electricity use by 17.8%without any upfront investment and major technical *** findings emphasize the important role of perceived control on occupant satisfaction and productivity,as well as on the energy-saving potential of the user-in-the-loop automation of buildings.

关键词： Autonomy Occupant behavior Comfort preference building energy management Human-in-the-loop automation

Machine Learning and Deep Learning Methods for Enhancing building energy Efficiency and Indoor Environmental Quality – A Review

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energy and AI 2022年第4期10卷 262-289页

作者： Paige Wenbin Tien Shuangyu Wei Jo Darkwa Christopher Wood John Kaiser Calautit Department of Architecture and Built Environment University of NottinghamNottingham NG72RDUK

The built environment sector is responsible for almost one-third of the world’s final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in

关键词： Artificial intelligence building energy management Deep learning Heating,ventilation and air conditioning (HVAC) Indoor environmental quality(IEQ) Machine learning Occupancy detection Thermal comfort

Novel machine learning paradigms-enabled methods for smart building operations in data-challenging contexts:Progress and perspectives

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National Science Open 2024年第3期3卷 88-109页

作者： Cheng Fan Yutian Lei Jinhan Mo Huilong Wang Qiuting Wu Jiena Cai Key Laboratory for Resilient Infrastructures of Coastal Cities Ministry of EducationShenzhen UniversityShenzhen 518067China Sino-Australia Joint Research Center in BIM and Smart Construction Shenzhen UniversityShenzhen 518067China College of Civil and Transportation Engineering Shenzhen UniversityShenzhen 518067China

The increasing availability of building operational data has greatly encouraged the development of advanced data-driven technologies for smart building *** operational data typically suffer from data quality problems,such as insufficient labeled and imbalanced data,making them incompatible with conventional machine learning *** advances in data science have provided novel machine learning paradigms to tackle such data challenges for practical applications,such as transfer learning,semi-supervised learning,and generative *** review aims to present the progress and perspectives on the effective utilization of novel machine learning paradigms for three major building energy management tasks,i.e.,building energy predictions,fault detection and diagnosis,and control ***-depth discussions have been provided to demonstrate the pros and cons of different learning approaches in terms of data compatibility,modeling difficulties,and possible application scenarios,which could be helpful for enhancing the feasibility of data-driven technologies for smart building operations.

关键词： smart building operations building energy management transfer learning semi-supervised learning generative learning