| 摘要: |
| 针对冷水机组运行能效模型结构复杂、受运行参数影响较大、机理建模困难等问题，本文建立了基于支持向量回归机的冷水机组运行能效预测模型，并采用粒子群优化算法对模型参数寻优，提高了模型的精度。论文以某商场中央空调离心式冷水机组为研究对象，随机选取396组运行数据对建立的模型进行训练和测试。结果表明，基于粒子群算法优化的冷水机组支持向量回归机模型较BP神经网络模型具有较高的预测精度，其相对误差基本上在3%以内。最后分别采集夏季和过渡季两日的运行数据验证模型的有效性，验证相对误差均在5%以内，因此，该模型能准确地反应冷水机组的运行能效，为冷水机组运行能效分析、故障诊断以及优化控制等提供参考依据。 |
| 关键词: 冷水机组 运行能效 预测模型 支持向量回归机 粒子群算法 |
| DOI： |
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| 基金项目:“十二五”国家支撑计划（2012BAD38B09）资助项目。 |
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| Research on COP Prediction Model of Chiller Based on PSO-SVR |
| Zhou Xuan,Cai Panpan,Lian Sizhen,Yan Junwei |
| (School of Mechanical & Automotive Engineering, South China University of Technology) |
| Abstract: |
| Since the difficulty of building mechanism model and the structure of COP model of chiller is complex, greatly affected by operating parameter, a COP prediction model of chiller is proposed based on Support Vector Regression, and the parameters are optimized by Particle Swarm Optimization algorithm. In this paper, 396 sets of operating data of chiller of a shopping mall are randomly selected to train and test this model. The results shows that the prediction accuracy of SVR model based on PSO optimization algorithm is higher than that of BP neural network and the relative error is within 3%. At last, operating data of two days in summer and transition season are randomly selected to verify the model. The relative error is within 5%. So this model can provide theoretical basis for the chiller energy efficiency analysis, fault detection and diagnosis and optimizing control. |
| Key words: chiller COP prediction model support vector regression particle swarm optimization |
