Analysis of outlier detection rules based on the ASHRAE global thermal comfort database

Zhang, Shaoxing; Yao, Runming; Du, Chenqiu; Essah, Emmanuel; Li, Baizhan

doi:10.1016/j.buildenv.2023.110155

Cited by 12 publications

(4 citation statements)

References 125 publications

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“…The database includes data from thermal comfort field investigations conducted worldwide from 1995 until the latest version on July 15, 2022. It comprises approximately 107,584 sets of objective indoor climatic observations, along with subjective evaluations provided by building occupants [20].…”

Section: Dataset and Key Parametersmentioning

confidence: 99%

Predicting Thermal Preferences - A Comparative Analysis of Machine Learning Algorithms using ASHRAE Global Thermal Comfort Database II

Omar Ahmed Al-Sharif,

Ahmed Emam Newir,

Mohamed Aly Halawa

2024

ARFMTS

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Predicting thermal preferences and ensuring comfort through machine learning is a highly active research field that has attracted significant attention from researchers aiming to achieve accurate forecasting and a deeper understanding of human thermal comfort in buildings. The primary objective of this study is to develop machine learning models for predicting thermal preference using the ASHRAE Global Thermal Comfort Database II. Additionally, the algorithms developed in this study can serve as valuable groundwork for transferring the acquired knowledge to develop personalized machine learning models, thereby enhancing individualized comfort. To enhance the dataset's accuracy and reliability, rigorous data exploration and preprocessing were executed. A comparative analysis of diverse machine learning algorithms was conducted, revealing that ensemble-based methods, namely Random Forest, Extra Trees, LightGBM, CatBoost, Gradient Boosting Machine, and XGBoost, exhibited superior performance in predicting thermal preferences. The accuracy of these ensemble models was further refined through hyperparameter optimization using the Optuna framework. This optimization led to a notable improvement, increased the model accuracy from 65% for traditional machine learning algorithms to 70% for the optimized ensemble algorithms.

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Section: Dataset and Key Parametersmentioning

confidence: 99%

Predicting Thermal Preferences - A Comparative Analysis of Machine Learning Algorithms using ASHRAE Global Thermal Comfort Database II

Omar Ahmed Al-Sharif,

Ahmed Emam Newir,

Mohamed Aly Halawa

2024

ARFMTS

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“…American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) dataset (Zhang et al, 2023b): This dataset contains valuable information on building energy consumption, indoor environmental conditions and other important factors. This valuable resource enables researchers and professionals in the field to gain insights into building energy consumption patterns and indoor environments.…”

Section: Datasetsmentioning

confidence: 99%

Research on construction and management strategy of carbon neutral stadiums based on CNN-QRLSTM model combined with dynamic attention mechanism

Ma,

2023

Front. Ecol. Evol.

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IntroductionLarge-scale construction projects such as sports stadiums are known for their significant energy consumption and carbon emissions, raising concerns about sustainability. This study addresses the pressing issue of developing carbon-neutral stadiums by proposing an integrated approach that leverages advanced convolutional neural networks (CNN) and quasi-recurrent long short-term memory (QRLSTM) models, combined with dynamic attention mechanisms.MethodsThe proposed approach employs the CNN-QRLSTM model, which combines the strengths of CNN and QRLSTM to handle both image and sequential data. Additionally, dynamic attention mechanisms are integrated to adaptively adjust attention weights based on varying situations, enhancing the model's ability to capture relevant information accurately.ResultsExperiments were conducted using four datasets: EnergyPlus, ASHRAE, CBECS, and UCl. The results demonstrated the superiority of the proposed model compared to other advanced models, achieving the highest scores of 97.79% accuracy, recall rate, F1 score, and AUC.DiscussionThe integration of deep learning models and dynamic attention mechanisms in stadium construction and management offers a more scientific decision support system for stakeholders. This approach facilitates sustainable choices in carbon reduction and resource utilization, contributing to the development of carbon-neutral stadiums.

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“…In this paper, outliers are examined using the principle of box plot, which is a statistical graph used to display the dispersion of a set of data [31], and its structure is shown in Fig 2 . The box plot provides a criterion for identifying outliers, i.e., a value greater than or less than the upper and lower bounds set by the box plot is considered an outlier.…”

Section: Test For Outliersmentioning

confidence: 99%

Spatial and temporal characteristics analysis and prediction model of PM2.5 concentration based on SpatioTemporal-Informer model

Luo

Jiang

et al. 2023

PLoS ONE

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The primary cause of hazy weather is PM2.5, and forecasting PM2.5 concentrations can aid in managing and preventing hazy weather. This paper proposes a novel spatiotemporal prediction model called SpatioTemporal-Informer (ST-Informer) in response to the shortcomings of spatiotemporal prediction models commonly used in studies for long-input series prediction. The ST-Informer model implements parallel computation of long correlations and adds an independent spatiotemporal embedding layer to the original Informer model. The spatiotemporal embedding layer captures the complex dynamic spatiotemporal correlations among the input information. In addition, the ProbSpare Self-Attention mechanism in this model can focus on extracting important contextual information of spatiotemporal data. The ST-Informer model uses weather and air pollutant concentration data from numerous stations as its input data. The outcomes of the trials indicate that (1) The ST-Informer model can sharply capture the peaks and sudden changes in PM2.5 concentrations. (2) Compared to the current models, the ST-Informer model shows better prediction performance while maintaining high-efficiency prediction (MAE≈7.50μg/m3,RMSE≈4.31μg/m3,R2≈0.88). (3) The ST-Informer model has universal applicability, and the model was applied to the concentration of other pollutants prediction with good results.

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Analysis of outlier detection rules based on the ASHRAE global thermal comfort database

Cited by 12 publications

References 125 publications

Predicting Thermal Preferences - A Comparative Analysis of Machine Learning Algorithms using ASHRAE Global Thermal Comfort Database II

Predicting Thermal Preferences - A Comparative Analysis of Machine Learning Algorithms using ASHRAE Global Thermal Comfort Database II

Research on construction and management strategy of carbon neutral stadiums based on CNN-QRLSTM model combined with dynamic attention mechanism

Spatial and temporal characteristics analysis and prediction model of PM2.5 concentration based on SpatioTemporal-Informer model

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