Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm

Tian, Zhen; Gan, Wanlong; Zou, Xianzhi; Zhang, Yuan; Gao, Wenliang

doi:10.1016/j.energy.2022.124027

Cited by 31 publications

(3 citation statements)

References 47 publications

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“…The BPNN algorithm optimized by GA is a continuous and iterative process used for searching the best weight and threshold [ 19 ]. The process of the BPNN algorithm optimized by GA is shown in Figure 2 .…”

Section: Suggestions and Methodologymentioning

confidence: 99%

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Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Xiao

Wang

et al. 2023

Sensors

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High-sensitivity early fire detection is an essential prerequisite to intelligent building safety. However, due to the small changes and erratic fluctuations in environmental parameters in the initial combustion phase, it is always a challenging task. To address this challenge, this paper proposes a hybrid feature fusion-based high-sensitivity early fire detection and warning method for in-building environments. More specifically, the temperature, smoke concentration, and carbon monoxide concentration were first selected as the main distinguishing attributes to indicate an in-building fire. Secondly, the propagation neural network (BPNN) and the least squares support vector machine (LSSVM) were employed to achieve the hybrid feature fusion. In addition, the genetic algorithm (GA) and particle swarm optimization (PSO) were also introduced to optimize the BPNN and the LSSVM, respectively. After that, the outputs of the GA-BPNN and the PSO-LSSVM were fused to make a final decision by means of the D-S evidence theory, achieving a highly sensitive and reliable early fire detection and warning system. Finally, an early fire warning system was developed, and the experimental results show that the proposed method can effectively detect an early fire with an accuracy of more than 96% for different types and regions of fire, including polyurethane foam fire, alcohol fire, beech wood smolder, and cotton woven fabric smolder.

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Section: Suggestions and Methodologymentioning

confidence: 99%

“…Due to the rapid development of machine learning, many intelligent algorithms have been presented to fuse several fire feature parameters [ 19 , 20 , 21 , 22 ]. This method overcomes the singularity and instability of the traditional threshold judgment method, which can significantly improve the accuracy of fire detection.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Xiao

Wang

et al. 2023

Sensors

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“…The model has high accuracy and reliability and can be used to optimize energy utilization in mobile edge computing, reducing the load on the power grid. Finally, Tian et al (2022) 12 used a genetic algorithm to optimize the BP neural network and improve the performance of a condensing organic Rankine cycle. The experimental results demonstrated high prediction accuracy and significant implications for energy conservation and environmental protection, providing new directions for sustainable development in green energy.…”

Section: Related Workmentioning

confidence: 99%

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

Yang,

Hu,

Wang

et al. 2024

Sci Rep

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This study addresses the drawbacks of traditional methods used in meter coefficient analysis, which are low accuracy and long processing time. A new method based on non-parametric analysis using the Back Propagation (BP) neural network is proposed to overcome these limitations. The study explores the classification and pattern recognition capabilities of the BP neural network by analyzing its non-parametric model and optimization methods. For model construction, the study uses the United Kingdom Domestic Appliance-Level Electricity dataset’s meter readings and related data for training and testing the proposed model. The non-parametric analysis model is used for data pre-processing, feature extraction, and normalization to obtain the training and testing datasets. Experimental tests compare the proposed non-parametric analysis model based on the BP neural network with the traditional Least Squares Method (LSM). The results demonstrate that the proposed model significantly improves the accuracy indicators such as mean absolute error (MAE) and mean relative error (MRE) when compared with the LSM method. The proposed model achieves an MAE of 0.025 and an MRE of 1.32% in the testing dataset, while the LSM method has an MAE of 0.043 and an MRE of 2.56% in the same dataset. Therefore, the proposed non-parametric analysis model based on the BP neural network can achieve higher accuracy in meter coefficient analysis when compared with the traditional LSM method. This study provides a novel non-parametric analysis method with practical reference value for the electricity industry in energy metering and load forecasting.

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Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification

et al. 2023

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Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm

Cited by 31 publications

References 47 publications

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Hybrid Feature Fusion-Based High-Sensitivity Fire Detection and Early Warning for Intelligent Building Systems

Construction and optimization of non-parametric analysis model for meter coefficients via back propagation neural network

Enhanced whale optimization algorithm-based modeling and simulation analysis for industrial system parameter identification

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