2023 Vol.6 Oct.N05

• Title: Deep neural network based chiller energy saving control method
• Name: HongLei Jing
• Company: Department of Information and Electromechanical engineering, Zhengzhou Business University, Zhengzhou 451200, China;
• Abstract:

  The current conventional energy-saving control of chiller plants mainly realizes distributed energy-saving control by mathematical modeling of the energy consumption node model of the plant, which leads to poor energy-saving control due to the lack of reasonable regulation of the cooling capacity. In this regard, a deep neural network-based chiller plant energy-saving control method is proposed. A gray box model combining mechanism analysis and least squares method is used to model the chiller plant and cooling tower equipment, and the two are coupled to calculate the maximum cooling capacity. And the chiller system parameters are identified and estimated, and finally the energy-saving optimal control model is constructed by combining the network algorithm. In the experiments, the energy-saving control performance of the proposed method is verified. The final test results show that when the proposed method is used to control the chiller plant for energy saving, the energy consumption value of the unit is significantly reduced and has a more desirable energy saving control performance.

  
  

• Keyword: deep neural network; chiller; energy saving control; cooling capacity;
• DOI: 10.12250/jpciams2023090716
• Citation form: HongLei Jing.Deep neural network based chiller energy saving control method [J]. Computer Informatization and Mechanical System,2023,Vol.6,pp.73-78

References

[1] Xia Y , Ding Q , Jing N ,et al. An enhanced fault detection method for centrifugal chillers using kernel density estimation based kernel entropy component analysis[J].International Journal of Refrigeration, 2021, 129(2):290-300.

[2] Liu H .Energy saving optimization and control for train unmanned driving systems [J].Unmanned Driving Systems for Smart Trains, 2021:253-295.

[3] Jung H H , Seon J Y , Lim T H ,et al. A Study of an Air Scouring Integral Immersed Hollow Fiber Membrane Module for Energy Saving and Membrane Fouling Control [J].Journal of Korean Society of Environmental Engineers, 2021, 43(7):547-556.

[4] Wang W , Du W , Cheng C ,et al. Output feedback control for energy-saving asymmetric hydraulic servo system based on desired compensation approach [J]. Applied Mathematical Modelling, 2022, 101:360-379.

[5] Nguyen H D , Nguyen Q H , Du Q V V ,et al. A novel combination of Deep Neural Network and Manta Ray Foraging Optimization for flood susceptibility mapping in Quang Ngai province, Vietnam[J].Geocarto International, 2021:1-22.

[6] Zhang K .Music Style Classification Algorithm Based on Music Feature Extraction and Deep Neural Network[J].Wireless Communications and Mobile Computing, 2021, 2021(4):1-7.

[7] Du C , Du C , Huang L ,et al. Structured Neural Decoding With Multitask Transfer Learning of Deep Neural Network Representations[J].IEEE transactions on neural networks and learning systems, 2022, 33(2):600-614.

[8] Xiang Y , Lv Y , Lei W ,et al. Ultra-Short Wave Communication Squelch Algorithm Based on Deep Neural Network [J].Big Data Mining and Analytics, 2023, 6(1 ):106-114.

[9] A C B , A C C , A G P ,et al. Designing a Deep Neural Network for an Acousto-Ultrasonic Investigation on the Corrosion behaviour of CORTEN Steel [J]. Procedia Structural Integrity, 2022, 37:307-313.

[10] Patel J M , Kumar K , Envelope S C P * .Development of Wiener-Hammerstein Models Parameterized using Orthonormal Basis Filters and Deep Neural Network [J].IFAC-PapersOnLine, 2022, 55( 7):94-99.

[11] Mu L , Chen Z , Liu L .Solving the linear transport equation by a deep neural network approach [J].Discrete and Continuous Dynamical Systems - S, 2022. 15(4):669-686.

[12] Laghuvarapu S , Modee R , Priyakumar U D .Benchmark study on deep neural network potentials for small organic molecules[J].Journal of Computational Chemistry, 2022, 43(5):308-318.

[13] Magnusson R ,Tegnér, Jesper N, Gustafsson M .Deep neural network prediction of genome-wide transcriptome signatures - beyond the Black-box[J]. npj Systems Biology and Applications, 2022, 8(1):1-8.

[14] Chen L , Xiong H , Sang X ,et al. An innovative deep neural network-based approach for internal cavity detection of timber columns using percussion sound:[J].Structural Health Monitoring, 2022, 21(3):1251-1265.

[15] Li G , Sun Y , Li L , et al. Simulation of Situational Sensing of Flow Information of Hydropower Unit Based on SG-UAP [J] Computer Simulation,2021(5) 68- 71,460.