Domain adaptation deep learning and its T-S diagnosis networks for the cross-control and cross-condition scenarios in data center HVAC systems

Du, Zhimin; Liang, Xinbin; Chen, Siliang; Li, Pengcheng; Zhu, Xu; Chen, Kang; Jin, Xinqiao

doi:10.1016/j.energy.2023.128084

Cited by 11 publications

(2 citation statements)

References 32 publications

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“…Generally, in the studies dealing with DL, researchers focused on user preferences and not on the number of people in the building, which could be empty, full, or partially occupied. Enhancing the use of energy in buildings has been extensively researched in the literature, particularly in managing HAVC systems by incorporating DL techniques, as clearly revised in the studies [24][25][26][27][28][29].…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Power Management Models for Buildings: A Comparative Analysis

Talib,

Croock

2024

JESA

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Power management in several sectors poses the problem of conserving the consumed power while satisfying the imposed conditions It is considered as a proactive control and management of the organization's energy consumption to save use and reduce energy expenses. Therefore, there is an actual need to include smart energy management systems in buildings in order to reduce the consumed energy. In this work, a comparative analysis is presented to evaluate deep and machine-learning approaches in the context of intelligent models for conserving power in smart buildings. The deep learning model is structured by using Deep Neural Networks (DNN), while machine learning models are represented by Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Naive Bayes (NB). These models adopt three classes: full (power in full consumption), select (power in partial consumption), and shout down (no power consumption). Moreover, feature reduction methods of Boruta and Principal Component Analysis (PCA) are implemented to reduce the complexity of the models. The proposed models are trained and tested using a measured dataset for a building. Comparison results of the proposed models showed that the Random Forest attracts more attention regarding classification accuracy by 100% and a reasonable classification time of 1.23 seconds. The effectiveness of the comparative analysis which indicating the highest accuracy results for RF makes it as a suitable model to be implemented as an optimal one in real-time power management systems.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Power Management Models for Buildings: A Comparative Analysis

Talib,

Croock

2024

JESA

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“…The methodology allows for a comprehensive assessment of qualitative risks, using the combined fault tree and event tree to represent multiple scenarios. The risk assessments rely on the Bayesian belief network (see Figure 5) [15][16][17][18].…”

Section: Formulation Hazard Identification and Development Of An Faul...mentioning

confidence: 99%

A Bayesian Network Model for Risk Management during Hydraulic Fracturing Process

Badjadi,

Zhu,

Zhang

et al. 2023

Water

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The escalating production of shale gas and oil, witnessed prominently in developed nations over the past decade, has sparked interest in prospective development, even in developing countries like Algeria. However, this growth is accompanied by significant opposition, particularly concerning the method of extraction: hydraulic fracturing, or ‘fracking’. Concerns regarding its environmental impact, water contamination, greenhouse gas emissions, and potential health effects have sparked widespread debate. This study thoroughly examines these concerns, employing an innovative approach to assess the risks associated with hydraulic fracturing operations in shale gas reservoirs. Through the integration of diverse data sources, including quantitative and qualitative data, observational records, expert judgments, and global sensitivity analysis using the Sobol method, a comprehensive risk assessment model, was developed. This model carefully considered multiple condition indicators and extreme working conditions, such as pressures exceeding 110 MPa and temperatures surpassing 180° F. The integration of these varied data streams enabled the development of a robust Bayesian belief network. This network served as a powerful tool for the accurate identification of process vulnerabilities and the formulation of optimal development strategies. Remarkably, this study’s results showed that this approach led to a notable 12% reduction in operational costs, demonstrating its practical efficacy. Moreover, this study subjected its model to rigorous uncertainty and sensitivity analyses, pinpointing the most severe risks and outlining optimal measures for their reduction. By empowering decision-makers to make informed choices, this methodology not only enhances environmental sustainability and safety standards but also ensures prolonged well longevity while maximizing productivity in hydraulic fracturing operations.

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