Analysis of Emergency Demand Response Levels of Central Air-Conditioning

Qi, Xianjun; Ji, Zongshuo; Wu, Hongbin; Zhang, Jingjing; Yang, Hanbiao

doi:10.1007/s42835-020-00525-3

Cited by 5 publications

(1 citation statement)

References 24 publications

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“…In the Western countries, air conditioning was widely used relatively earlier. So, the research on energy-saving methods of air conditioning started earlier, with a long development stage and relatively mature technology [4]. Some scholars investigated the comfort control method of air conditioning in building energy saving in 2000. e problem that how to use optimized control strategies to reduce energy consumption on the premise of ensuring comfort when there is a con ict between energy saving and comfort mainly was investigated [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Energy-Saving Control Method of Building Central Air Conditioning Based on Genetic Algorithm

Wang

2022

Advances in Multimedia

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In order to solve the problem of huge energy consumption of building central air conditioning, the water system of building central air conditioning was taken as the research object and the genetic algorithm of energy-saving operation and control of building central air conditioning based on MATLAB was designed, so as to build the energy-saving control system model of central air conditioning. On the one hand, the feasibility of the optimized algorithm to solve the energy-saving problem of central air conditioning was proved through the optimization of genetic algorithm. On the other hand, the energy-saving effect was demonstrated through the operation parameters and the variation of air-conditioning energy consumption before and after the experiment. The feasibility and rationality of genetic algorithm to improve the energy-saving effect of central air conditioning was verified by the comparison of the experiment. The results showed that in the design load range of 30%–70%, the average energy-saving rate of central air-conditioning compressor was 16.8%. The maximum energy-saving efficiency was 37.4%, and the minimum energy-saving efficiency was 15.5%. The average energy-saving rate of water system was 26.8%. Therefore, the genetic algorithm had fast convergence speed and high solving accuracy, which was beneficial to improve the energy-saving ability of central air-conditioning water system.

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Section: Literature Reviewmentioning

confidence: 99%