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基于MSTSO算法的冷水机组负荷分配模型研究

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为降低空调系统的运行能耗，优化冷水机组的负荷分配，提出了一种多策略改进的金枪鱼优化算法(MSTSO)，引入黄金正弦觅食机制和非线性惯性权重来加强算法对最优解的全局定位能力;通过蜜獾随机搜索策略赋予算法更强的性能以跳出局部最优;利用双向长短期记忆网络(BiLSTM)搭建能效预测模型并对各机组的能效比(COP)进行预测，同时使用MSTSO算法对网络的初始参数进行寻优从而获得最佳训练效果;提出BiLSTM-MSTSO负荷分配模型，对多台冷水机组的部分负荷率(PLR)进行合理分配与优化;实验结果表明，优化后的BiLSTM预测模型拥有更高的预测精度，MSTSO算法相较其他智能优化算法可以减少更多的能耗并最大化提升冷水机组的运行效率;因此BiLSTM-MSTSO智能模型适用于多冷水机组的能耗预测与优化。
Study on Load Distribution Model of Chillers Based on MSTSO Algorithm
To reduce the operating energy consumption of air-conditioning systems and optimize the load distribution of chillers,a multi-strategy improved tuna swarm optimization algorithm(MSTSO)is proposed,A golden sine foraging mechanism and non-linear inertia weights are introduced to enhance the algorithm's ability to locate the optimal solution globally.A honey badger random search strategy is used to help the algorithm stronger performance to jump out of the local optimum.A bi-directional long short-term memory(BiLSTM)network is used to build an energy efficiency prediction model and predict the coefficient of performance(COP)of each chiller,while the MSTSO algorithm is used to optimize the initial parameters of the network to obtain the best training results.A BiLSTM-MSTSO energy consumption optimization model is proposed to reasonably allocate and optimize the part load ratio(PLR)of multi-chillers.The experimental results show that the optimized BiLSTM prediction model has higher prediction accuracy,and com-pared with other intelligent optimization algorithms,the MSTSO algorithm can reduce the energy consumption and maximize the oper-ating efficiency of chillers.Therefore,the BiLSTM-MSTSO intelligent model can be used to predict and optimize the energy con-sumption of multi-chillers.

multi-chillersload distributiontuna swarm optimization algorithmbi-directional long short-term memory networkenergy consumption optimization

王华秋、李乐天

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重庆理工大学两江人工智能学院,重庆 401135

多冷水机组 负荷分配 金枪鱼优化算法 双向长短期记忆网络 能耗优化

国家科技部重点研发计划项目重庆市科委一般自然基金项目

2018YFB1700803cstc2019jcyjmsxmX0500

2024

10.16526/j.cnki.11-4762/tp.2024.01.029

计算机测量与控制
中国计算机自动测量与控制技术协会

计算机测量与控制

CSTPCD
影响因子：0.546
ISSN：1671-4598
年,卷(期)：2024.32(1)
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