Predictive control model for variable air volume terminal valve opening based on backpropagation neural network

Feng, Guozeng; Lei, Shuya; Gu, Xinxin; Guo, Yuejiao; Wang, Junyi

doi:10.1016/j.buildenv.2020.107485

Cited by 20 publications

(3 citation statements)

References 29 publications

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“…A direct correlation between the minimum variable air volume (VAV) and thermal comfort was previously established [14], and for that reason, Feng et al sought to create a prediction model for the valve opening of the VAV terminal based on a back propagation neural network to improve thermal comfort. They collected the actual data and trained using the newff; the results show that the error between the expected and the predicted air volume is less than 5% [15]. Ahn and Cho developed a multilayer perception-based (MLP) ANN model to optimize the supply of air conditioning to provide thermal comfort for district heating systems.…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

Çakır

Akbulut

2022

IEEE Access

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To create and maintain comfortable indoor environments, predicting occupant thermal sensation is an important goal for architects, engineers, and facility managers. The link between thermal comfort, productivity, and health is common knowledge, and researchers have developed many state-of-theart thermal-sensation models from dozens of research projects over the last 50 years. In addition to these, the use of intelligent data-analysis techniques, such as black-box artificial neural networks (ANNs), is receiving research attention with the aim of designing building thermal-behavior models from collected data. With the convergence of the internet of things (IoT), cloud computing, and artificial intelligence (AI), smart buildings now protect us and keep us comfortable while saving energy and cutting emissions. These types of smart buildings play a vital role in building smart cities of the future. The aim of this study is to help facility managers predict the thermal sensation of the occupants under the given circumstances. To achieve this, we applied a data-driven approach to predict the thermal sensation of occupants of an indoor environment using previously collected data. Our main contribution is to design and evaluate a deep neural network (DNN) for predicting thermal sensations with a high degree of accuracy regardless of building type, climate zone, or a building's heating and/or ventilation methods. We used the second version of the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database to train our model. The hyperparameter-tuning process of the proposed model is optimized using the Bayesian strategy and predicts the thermal sensation of occupants with 78% accuracy, which is much higher than the traditional predicted mean vote (PMV) model and the other shallow and deep networks compared.

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Section: Related Workmentioning

confidence: 99%

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

Çakır

Akbulut

2022

IEEE Access

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“…Yuhong Dong et al [7] proposed a wavelet-BP neural network-based method for accurate fertilization of maize, which effectively extracted information about soil nutrients, fertilization, and yield from the original signal by wavelet transform, and combined wavelet analysis with an optimized BP neural network to achieve better accuracy of fertilization prediction. Guozeng Feng et al [8] proposed a BP neural network-based valve-opening prediction model and tested the prediction of the model under different conditions. The results showed that the approximation capability of the neural network model can be used to directly output the position of the demand valve at the VAV terminal, reducing the convergence time and stabilization time.…”

Section: Introductionmentioning

confidence: 99%

Research and Design of Precision Fertilizer Application Control System Based on PSO-BP-PID Algorithm

et al. 2022

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China has had the highest fertilizer use rate in the world for years, but today a large number of farmlands still use traditional manual fertilizer application methods, which rely entirely on personal experience and not only cause the waste of fertilizer and water resources but also make the local ecological environment polluted. This paper researches and designs a BP neural network PID controller based on PSO optimization to address the above problems. The PSO algorithm is used to optimize the initial weights of the BP neural network, and then optimize the control parameters of the PID to achieve accurate control of the liquid fertilizer flow. A precision fertilizer control system based on the STM32 microcontroller was also developed, and the performance of this controller was verified in tests. The results showed that compared with the conventional PID controller and BP neural network-based PID controller, this controller had good control accuracy and robustness, the average maximum overshoot was 6.35%, and the average regulation time was 41.17 s; when the fertilizer application flow rate was 0.6 m3/h, the shortest adjustment time is 30.85 s, which achieves the effect of precise fertilizer application.

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“…Neural networks have been applied in many fields, such as space-based large mirror structure, turbine disks, automotive bushings, etc. As a machine learning method, the backpropagation neural network (BPNN) has great advantages in fitting nonlinear functions [ 26 , 27 ]. BPNNs have a strong function approximation ability, which can be approximated to continuous functions with arbitrary accuracy.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

Zhang

Sun

2021

Materials

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Fit of the highly nonlinear functional relationship between input variables and output response is important and challenging for the optical machine structure optimization design process. The backpropagation neural network method based on particle swarm optimization and Bayesian regularization algorithms (called BMPB) is proposed to solve this problem. A prediction model of the mass and first-order modal frequency of the supporting structure is developed using the supporting structure as an example. The first-order modal frequency is used as the constraint condition to optimize the lightweight design of the supporting structure’s mass. Results show that the prediction model has more than 99% accuracy in predicting the mass and the first-order modal frequency of the supporting structure, and converges quickly in the supporting structure’s mass-optimization process. The supporting structure results demonstrate the advantages of the method proposed in the article in terms of high accuracy and efficiency. The study in this paper provides an effective method for the optimized design of optical machine structures.

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Predictive control model for variable air volume terminal valve opening based on backpropagation neural network

Cited by 20 publications

References 29 publications

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

A Bayesian Deep Neural Network Approach to Seven-Point Thermal Sensation Perception

Research and Design of Precision Fertilizer Application Control System Based on PSO-BP-PID Algorithm

Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and Bayesian Regularization Algorithms

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