基于PSO的SVR模型在多联机功耗预测上的应用

李昱瑾; 陈焕新; 刘江岩

doi:10.3969/j.issn.0253-4339.2019.06.053

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基于PSO的SVR模型在多联机功耗预测上的应用

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- 基于PSO的SVR模型在多联机功耗预测上的应用
- Optimized Support Vector Regression Model Based on Particle Swarm Optimization for Energy Consumption Prediction of a Variable Refrigerant Flow System
- 制冷学报 2019年40卷第6期
- 作者机构：
  
  1. 华中科技大学中欧清洁与可再生能源学院
  2. 华中科技大学能源与动力工程学院
- 作者简介：
- 基金信息：
  
  国家自然科学基金（51876070，51576074）资助项目。
- DOI：10.3969/j.issn.0253-4339.2019.06.053
  中图分类号：
- 纸质出版日期：2019
- 稿件说明：
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李昱瑾, 陈焕新, 刘江岩. 基于PSO的SVR模型在多联机功耗预测上的应用[J]. 制冷学报, 2019,40(6).

Li Yujin, Chen Huanxin, Liu Jiangyan. Optimized Support Vector Regression Model Based on Particle Swarm Optimization for Energy Consumption Prediction of a Variable Refrigerant Flow System[J]. Journal of refrigeration, 2019, 40(6).
李昱瑾, 陈焕新, 刘江岩. 基于PSO的SVR模型在多联机功耗预测上的应用[J]. 制冷学报, 2019,40(6). DOI： 10.3969/j.issn.0253-4339.2019.06.053.

Li Yujin, Chen Huanxin, Liu Jiangyan. Optimized Support Vector Regression Model Based on Particle Swarm Optimization for Energy Consumption Prediction of a Variable Refrigerant Flow System[J]. Journal of refrigeration, 2019, 40(6). DOI： 10.3969/j.issn.0253-4339.2019.06.053.

摘要

支持向量回归（SVR）模型在多联机系统功耗预测稳定性和精度上存在不足，本文引入粒子群优化算法（PSO），对SVR预测模型的惩罚系数和核参数进行最优求解，来改善模型预测性能。在制冷剂充注量为95.75%工况下，对多联机组进行运行实验，并对实验数据进行预处理。基于PSO算法建立PSO-SVR模型，对多联机功耗进行预测，并与SVR模型的预测结果和理论公式计算结果进行比较。结果表明：SVR、PSO-SVR、理论公式计算法总体预测误差分别为1.43%、1.08%和1.57%，均方根误差RMSE分别为105.36、88.79、91.37 W，参数寻优结果为惩罚系数C=1 0000和核参数γ=4.275。粒子群优化算法的引入，显著提高了SVR模型的预测精度和稳定性；相较于理论公式计算法，PSO-SVR精度更高，且需要测量的参数数目明显减少，在降低了测量系统复杂性同时更具经济适用性。

Abstract

Energy consumption prediction analysis has important significance in energy management

operation strategy optimization

control optimization

etc. For variable refrigerant flow (VRF) systems

the pure support vector regression (SVR) prediction model has insufficient stability and prediction accuracy. By introducing the particle swarm optimization (PSO) algorithm

this study optimizes the selection of punish and nuclear parameters for a pure SVR prediction model and then compares the prediction results of the PSO-SVR model

pure SVR model

and theoretical formula. The results show that the overall prediction errors for SVR

PSO-SVR and theoretical formula are 1.43%

1.08% and 1.57%

and the root mean square error are 105.36 W

88.79 W and 91.37 W respectively. By solving for the best C and γ equal to 10

000 and 4.275

the PSO can significantly improve the performance and stability of the pure SVR prediction model. In addition

it demonstrated better results than those of the formula calculation method with less variables to be measured. It is reasonable to state that the PSO-SVR model is a convenient and economic means to solve such problems.

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