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空压机负荷预测与智能调度算法研究

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对目前空压机机组调度中存在能源消耗高、资源浪费严重等问题,结合空压机机组的组合特点,对空压机的台数调度进行研究.提出一种多策略改进的哈里斯鹰优化算法(MHHO)和深度回声状态网络(DESN)相组合的空压机负荷预测模型,在获得一天24 h所需负荷之后,利用MHHO算法对机组组合进行调度和用气量分配.实验结果表明:预测模型对空压机负荷预测具有更高的准确性,提高了机组的运行效率,减少了系统能源消耗,具有应用价值.
Research on air compressor load prediction and intelligent scheduling algorithm
The air compressor system consists of multiple units,and the optimization of unit combination is a nonlinear and large-scale task of multiple objectives and constraints.To address such problems as high energy consumption and serious waste of resources in air compressor scheduling,this paper studies the quantity scheduling problem of air compressors based on the characteristics of air compressor combination.A multi-strategy improved Harris Hawk Optimization Algorithm(MHHO)combined with Deep Echo State Network(DESN)is proposed to predict the load of the air compressor.After obtaining the load required for 24 hours a day,the MHHO algorithm is employed for unit combination scheduling and gas consumption allocation.Our experimental results show the prediction model achieves a higher prediction accuracy,and thus is highly applicable for air compressor load prediction.Intelligent scheduling improves the unit operation efficiency and reduces the system's energy consumption.

air compressor load predictionimproved Harris Hawk optimization algorithmdeep echo state networkhyperparametersintelligent scheduling

王华秋、张燕

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重庆理工大学两江人工智能学院,重庆 401135

空压机负荷预测 改进的哈里斯鹰优化算法 深度回声状态网络 超参数 智能调度

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

10.3969/j.issn.1674-8425(z).2024.01.018

重庆理工大学学报
重庆理工大学

重庆理工大学学报

CSTPCD北大核心
影响因子：0.567
ISSN：1674-8425
年,卷(期)：2024.38(1)
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