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• W4313731317 endingPage "105830" @default.
• W4313731317 startingPage "105830" @default.
• W4313731317 abstract "Air conditioning systems are playing an increasingly important role in our daily life, and the control of air conditioning systems is related to the intelligence of the system, indoor thermal comfort and energy consumption. Therefore, the control of air conditioning systems has always been an important research topic. Artificial neural network (ANN) and genetic algorithm (GA) are two commonly used intelligent control methods. However, there is no guidance until now on the applicability of these two control methods and how to make a choice between them in the control of air conditioning systems. In this study, an ANN control strategy and a GA control strategy were developed and experimentally verified. The experimental results indicated that both the ANN control strategy and the GA control strategy could control the air conditioning system properly under different control commands. To compare the control performances, the convergence speed, discrete characteristic, energy consumption and interference resistance ability were calculated or experimentally validated. The ANN control strategy showed better performances in the convergence speed and energy consumption. While the GA control strategy performed better in maintaining the stable state of the air conditioning system. The innovation of this study lies in two points. First, when designing the ANN control strategy and the GA control strategy, ANN and GA were applied as the central control algorithm, rather than only as auxiliary algorithms for system identification, prediction or optimization. This methodology is relatively novel in the design of ANN and GA control strategies for the air conditioning system, and could further expand the application of ANN and GA. Second, this study employed 4 evaluating indicators including convergence speed, discrete characteristic, energy consumption and interference resistance ability, to comprehensively evaluated the control performance of ANN control strategy and GA control strategy." @default.
• W4313731317 created "2023-01-08" @default.
• W4313731317 creator A5004163805 @default.
• W4313731317 date "2023-05-01" @default.
• W4313731317 modified "2023-09-27" @default.
• W4313731317 title "Comparison of the characteristics of the control strategies based on artificial neural network and genetic algorithm for air conditioning systems" @default.
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• W4313731317 doi "https://doi.org/10.1016/j.jobe.2023.105830" @default.
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