Dynamic prediction of the pre-dehumidification of a radiant floor cooling and displacement ventilation system based on computational fluid dynamics and a back-propagation neural network: A case study of an office room

Self Cite

Due to rapid global economic development, the number of motor vehicles has increased sharply, causing significant traffic pollution and posing a threat to people’s health. People’s exposure to traffic-related particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) primarily occurs during commuting. Many studies have used exposure risk assessment models to assess the possible adverse effects of PM2.5, but few have used them to plan low-risk pathways for commuters. This study simulated the pollutant concentration distribution in an idealized urban area in different scenarios. We then used a back propagation (BP) neural network to predict the pollutant concentration. The commuter respiratory deposition dose was calculated based on the BP prediction results, and the respiratory deposition dose was converted into obstacles on the commuting map. Finally, the rapidly exploring random tree star (RRT*) algorithm was used to plan low-risk paths for commuters. The results indicate that pollutants discharged by cars and tree planting can significantly affect the pollutant concentration. A 30.25 μg/m3 increase in the pollutant concentration discharged by cars resulted in a 7~13 μg/m3 increase in the traffic-related air pollution concentration on sidewalks. Combining a computational fluid dynamics simulation, a BP neural network model, and the RRT* algorithm provides a system to plan low-risk paths for commuters. This work proposes artificial-intelligence-based models for calculating the exposure risk to traffic-related pollutants (PM2.5) and choosing a low-risk commuting path to ensure healthy travel.

Section: Bp Neural Networkmentioning

confidence: 99%

Exposure Assessment of Traffic-Related Air Pollution Based on CFD and BP Neural Network and Artificial Intelligence Prediction of Optimal Route in an Urban Area

Ren

et al. 2022

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“…Radiant floor cooling has been widely studied due to its low operating noise, spacesaving design, low energy consumption, and thermal comfort [12][13][14][15]. Liu et al [16] conducted a comparative analysis of three case studies and concluded that hybrid radiant cooling systems were particularly effective in extremely hot and humid conditions, prevalent during summer.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System

Lu,

Cui,

Gao

et al. 2024

Machine learning algorithms have proven to be practical in a wide range of applications. Many studies have been conducted on the operational energy consumption and thermal comfort of radiant floor systems. This paper conducts a case study in a self-designed experimental setup that combines radiant floor and fan coil cooling (RFCFC) and develops a data monitoring system as a source of historical operational data. Seven machine learning algorithms (extreme learning machine (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector machine (SVM), and long short-term memory (LSTM)) were employed to predict the behavior of the RFCFC system. Corresponding prediction models were then developed to evaluate operative temperature (Top) and energy consumption (Eh). The performance of the model was evaluated using five error metrics. The obtained results showed that the RF model had very high performance in predicting Top and Eh, with high correlation coefficients (>0.9915) and low error metrics. Compared with other models, it also demonstrated high accuracy in Eh prediction, yielding maximum reductions of 68.1, 82.4, and 43.2% in the mean absolute percentage error (MAPE), mean squared error (MSE), and mean absolute error (MAE), respectively. A sensitivity ranking algorithm analysis was also conducted. The obtained results demonstrated the importance of adjusting parameters, such as the radiant floor supply water temperature, to enhance the indoor comfort. This study provides a novel and effective method for evaluating the energy efficiency and thermal comfort of radiant cooling systems. It also provides insights for optimizing the efficiency and thermal comfort of RFCFC systems, and lays a theoretical foundation for future studies integrating machine learning algorithms in this field.

“…Statistical methods tend to overfit when handling diverse influencing factors, leading to poor predictive performance of the models [4][5][6][7]. With the wide application of artificial intelligence algorithms in the prediction field [8][9][10][11], more and more scholars have started researching load-predicting methods founded on machine learning [12][13][14]. Some commonly used approaches in machine learning include artificial neural networks (ANN) [15], support vector machines [16], extreme learning machines [17], random forest (RF) [18], and regression trees [19].…”

Section: Introductionmentioning

confidence: 99%

Hourly Heat Load Prediction for Residential Buildings Based on Multiple Combination Models: A Comparative Study

An,

Zhu,

Yang

et al. 2023

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The accurate prediction of residential heat load is crucial for effective heating system design, energy management, and cost optimization. In order to further improve the prediction accuracy of the model, this study introduced principal component analysis (PCA), the minimum sum of squares of the combined prediction errors (minSSE), genetic algorithm (GA), and firefly algorithm (FA) into back propagation (BP) and ELMAN neural networks, and established three kinds of combined prediction models. The proposed methodologies are evaluated using real-world data collected from residential buildings over a period of one year. The obtained results of the PCA-BP-ELMAN, FA-ELMAN, and GA-BP models are compared with the neural network before optimization. The experimental results show that the combined prediction models have higher prediction accuracy. The Mean Absolute Percentage Error (MAPE) evaluation indices of the three combined models are distributed between 5.95% and 7.05%. The FA-ELMAN model is the combination model with the highest prediction accuracy, and its MAPE is 5.95%, which is 2.25% lower than the MAPE of an individual neural network. This research contributes to the field by providing a comprehensive and effective framework for residential heat load prediction, which can be valuable for building energy management and optimization.