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<p>This paper mainly studies the energy consumption of air conditioning and refrigeration system from the perspective of the dynamic operation of air conditioning systems and selects the energy consumption of a chiller system, cooling tower system, chilled water pump system and fan coil system, which affect the energy consumption of air conditioning system, as model variables. An integrated learning method based on a multilayer perceptron is proposed to integrate spatial features with temporal features, and a spatio-temporal prediction model for air conditioning energy consumption analysis is established under the Seq2Seq framework, and then the input curves of the four variables with load change under the optimal operation of the air conditioning system under dynamic load are derived. The results show that the deviation between the simulated value and the measured, calculated value of the energy consumption analysis model of air conditioning and refrigeration equipment based on the multilayer perceptron is within 5%, which can effectively and accurately predict and reduce air conditioning energy consumption by 15% to 20%. The maximum energy-saving efficiency can reach 30% when the air conditioning system is in optimal operation under dynamic load, and the energy-saving effect of the chiller, chilled water pump and cooling water pump is relatively significant. The research results of this paper have a certain reference value for reducing the energy consumption of air conditioning systems in practice.</p>
</abstract>

This paper mainly studies the energy consumption of air conditioning and refrigeration system from the perspective of the dynamic operation of air conditioning systems and selects the energy consumption of a chiller system, cooling tower system, chilled water pump system and fan coil system, which affect the energy consumption of air conditioning system, as model variables. An integrated learning method based on a multilayer perceptron is proposed to integrate spatial features with temporal features, and a spatio-temporal prediction model for air conditioning energy consumption analysis is established under the Seq2Seq framework, and then the input curves of the four variables with load change under the optimal operation of the air conditioning system under dynamic load are derived. The results show that the deviation between the simulated value and the measured, calculated value of the energy consumption analysis model of air conditioning and refrigeration equipment based on the multilayer perceptron is within 5%, which can effectively and accurately predict and reduce air conditioning energy consumption by 15% to 20%. The maximum energy-saving efficiency can reach 30% when the air conditioning system is in optimal operation under dynamic load, and the energy-saving effect of the chiller, chilled water pump and cooling water pump is relatively significant. The research results of this paper have a certain reference value for reducing the energy consumption of air conditioning systems in practice.

Research on energy consumption analysis of air conditioning and refrigeration equipment based on perceptron model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper mainly studies the energy consumption of air conditioning and refrigeration system from the perspective of the dynamic operation of air conditioning...

Research on energy consumption analysis of air conditioning and refrigeration equipment based on perceptron model

Pubblicato online: 20 set 2023

Pagine: -

Ricevuto: 24 ott 2022

Accettato: 31 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00347

Parole chiave
Air conditioning and refrigeration system, Multilayer perceptron, Integrated learning, Spatio-temporal prediction model, Dynamic load

© 2023 Youtong Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on energy consumption analysis of air conditioning and refrigeration equipment based on perceptron model

Pubblicato online: 20 set 2023

Pagine: -

Ricevuto: 24 ott 2022

Accettato: 31 mar 2023

DOI: https://doi.org/10.2478/amns.2023.2.00347

Parole chiaveAir conditioning and refrigeration system, Multilayer perceptron, Integrated learning, Spatio-temporal prediction model, Dynamic load

© 2023 Youtong Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Air conditioning and refrigeration system, Multilayer perceptron, Integrated learning, Spatio-temporal prediction model, Dynamic load