Leveraging graph convolutional networks for semi-supervised fault diagnosis of HVAC systems in data-scarce contexts

Interpretable mechanism mining enhanced deep learning for fault diagnosis of heating, ventilation and air conditioning systems

The application of data-driven building energy load prediction technologies remains a time-consuming effort, since it highly relies on human expertise to train data-driven building energy load prediction models. To address this issue, this study proposes an automated machine learning-based method which can develop accurate data-driven building energy load prediction models with little human assistance. The potential of six representative automated machine learning frameworks (AutoWeka, H2O, TPOT, AutoGluon, FLAML and AutoKeras) are investigated through utilizing them to forecast 1-h ahead heating, cooling and electrical loads of three real-world buildings. The results show that the proposed method outperforms manual modeling. The accuracy of the proposed method increases by 1.10%–18.66% compared to manual modeling. It is also demonstrated that AutoGluon and FLAML can obtain high prediction accuracy using short training times. Moreover, AutoWeka, H2O and TPOT usually need longer training times than AutoGluon and FLAML to develop high-accuracy prediction models. As for AutoKeras, it doesn't show high accuracy on building energy load prediction. This study provides guidelines for making data-driven building energy load prediction technologies easier to use in practical applications. 

Automated machine learning-based building energy load prediction method

Predicting energy consumption has become a critical issue for energy-intensive industrial contexts. A significant contribution to their overall energy load is due to the Heating Ventilation and Air Conditioning (HVAC) systems. This work, therefore, aims to validate the applicability of a probabilistic graphical approach, the Bayesian Network, in predicting the HVAC systems' energy consumption. As a data-driven approach, it is compared with more common AI-based models like Support Vector Machine, Artificial Neural Networks and Random Forest. The graphical approach ensures a better interpretation of the main factors determining the energy consumption and the relationships underlying these dependences. After an initial contextualisation and an analysis of the state of the art, the design methodology of a Bayesian network is investigated in detail, deepening in the various solutions for each step and evaluating their performance through the application on two industrial case studies. The results show that Bayesian networks, despite not always providing the best results, are a valid solution, trading off between simplicity, flexibility, and performance. Moreover, the possibility to provide a physical interpretation of the results is one of its main strengths. The critical aspect encountered, instead, is the need for discretisation, which strongly influences the quality of the results. 

Energy consumption prediction of industrial HVAC systems using Bayesian Networks

Large language models (LLMs) have shown human-level capabilities in solving various complex tasks. However, it is still unknown whether state-of-the-art LLMs master sufficient knowledge related to heating, ventilation and air conditioning (HVAC) systems. It will be inspiring if LLMs can think and learn like professionals in the HVAC industry. Hence, this study investigates the performance of LLMs on mastering the knowledge and skills related to the HVAC industry by letting them take the ASHRAE Certified HVAC Designer examination, an authoritative examination in the HVAC industry. Three key knowledge capabilities are explored: recall, analysis and application. Twelve representative LLMs are tested such as GPT-3.5, GPT-4 and LLaMA. According to the results, GPT-4 passes the ASHRAE Certified HVAC Designer examination with scores from 74 to 78, which is higher than about half of human examinees. Besides, GPT-3.5 passes the examination twice out of five times. It demonstrates that some LLMs such as GPT-4 and GPT-3.5 have great potential to assist or replace humans in designing and operating HVAC systems. However, they still make some mistakes sometimes due to the lack of knowledge, poor reasoning capabilities and unsatisfactory equation calculation abilities. Accordingly, four future research directions are proposed to reveal how to utilize and improve LLMs in the HVAC industry: teaching LLMs to use design tools or software in the HVAC industry, enabling LLMs to read and analyze the operational data from HVAC systems, developing tailored corpuses for the HVAC industry, and assessing the performance of LLMs in real-world HVAC design and operation scenarios. 

Evaluation of large language models (LLMs) on the mastery of knowledge and skills in the heating, ventilation and air conditioning (HVAC) industry

Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage

Probabilistic graphical models (PGMs) can effectively deal with the problems of energy consumption and occupancy prediction, fault detection and diagnosis, reliability analysis, and optimization in energy systems. Compared with the black-box models, PGMs show advantages in model interpretability, scalability and reliability. They have great potential to realize the true artificial intelligence in energy systems of the next generation. This paper intends to provide a comprehensive review of the PGM-based approaches published in the last decades. It reveals the advantages, limitations and potential future research directions of the PGM-based approaches for energy systems. Two types of PGMs are summarized in this review, including static models (SPGMs) and dynamic models (DPGMs). SPGMs can conduct probabilistic inference based on incomplete, uncertain or even conflicting information. SPGM-based approaches are proposed to deal with various management tasks in energy systems. They show outstanding performance in fault detection and diagnosis of energy systems. DPGMs can represent a dynamic and stochastic process by describing how its state changes with time. DPGM-based approaches have high accuracy in predicting the energy consumption, occupancy and failures of energy systems. In the future, a unified framework is suggested to fuse the knowledge-driven and data-driven PGMs for achieving better performances. Universal PGM-based approaches are needed that can be adapted to various energy systems. Hybrid algorithms would outperform the basic PGMs by integrating advanced techniques such as deep learning and first-order logic.

Probabilistic graphical models in energy systems: A review

A semantic model-based fault detection approach for building energy systems

pdf/simple-determination-of-dark-states-in-a-general-multi-level-3eb7e2kh.pdf

Simple determination of dark states in a general multi-level system

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Papers

Graph convolutional networks-based method for estimating design loads of complex buildings in the preliminary design stage

Probabilistic graphical models in energy systems: A review

A semantic model-based fault detection approach for building energy systems

Simple determination of dark states in a general multi-level system