| 引用本文: | 于军琪,边策,赵安军,解云飞,惠蕾蕾.考虑频域分解后数据特征的空调负荷预测模型[J].控制理论与应用,2022,39(6):1149~1157.[点击复制] |
| YU Jun-qi,BIAN Ce,ZHAO An-jun,XIE Yun-fei,HUI Lei-lei.Cooling load forecasting model considering data characteristics after frequency domain decomposition[J].Control Theory and Technology,2022,39(6):1149~1157.[点击复制] |
|
| 考虑频域分解后数据特征的空调负荷预测模型 |
| Cooling load forecasting model considering data characteristics after frequency domain decomposition |
| 摘要点击 1583 全文点击 857 投稿时间：2021-03-10 修订日期：2022-06-01 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2021.10206 |
| 2022,39(6):1149-1157 |
| 中文关键词 负荷预测 变分模态分解 最小二乘支持向量机 极端梯度提升 随机误差分析 |
| 英文关键词 load forecasting variational mode decomposition least-square support vector machine eXtremeGradient boosting random error analysis |
| 基金项目 国家重点研发计划项目(2017YFC0704100), 西安咸阳机场三期扩建工程绿色能源站系统智能管控咨询与顾问项目技术服务项目(20210103)资助. |
|
| 中文摘要 |
| 针对空调负荷预测实际应用中容易存在数据散杂且可用信息匮乏的问题, 从负荷序列的非线性、非平稳性
和随机性出发, 提出了一种基于变分模态分解(VMD)的负荷预测方法. 对不同数据特征序列考虑不同算法的数据观
测与训练原理差异, 充分发挥各个模型优势. 首先采用随机森林(RF)进行特征选择, 利用VMD将负荷序列按趋势分
量、平稳分量和噪声分量进行分类重构, 并分别对非线性序列建立最小二乘支持向量机(LSSVM)预测模型, 时序平
稳序列建立极端梯度提升(XGBoost)预测模型, 采用正态分布拟合随机误差, 得到各子序列预测结果并进行叠加输
出最终负荷预测结果. 实验结果表明, 所提方法能准确反映负荷的特性并具有更好的预测精度, 能有效预测空调负
荷, 为空调节能优化控制策略提供依据. |
| 英文摘要 |
| Aimed at the problems of scattered data and lack of available information in practical engineering application
of air conditioning cooling load forecasting, a load forecasting method based on Variational Mode Decomposition (VMD) is
proposed according to the nonlinear, non-stationary and random characteristics of the cooling load sequence. Considering
the differences of data observation and training principles of different algorithms for sequences showing different data
characteristics, the advantages of each model are fully utilized. Firstly, Random Forest (RF) is used for feature selection.
Then, VMD is applied to decompose cooling load series into the trend part, the stationary part and the noise part. Least-
Square Support Vector Machine (LSSVM) model is built for the nonlinear part, eXtremeGradient Boosting (XGBoost)
model is built for the linear part, and probability distribution is fitted for the noise part, following which the ultimate
cooling load forecast could be obtained by accumulating prediction values from each sub-series. The experimental results
show that the proposed method can comprehensively capture the characteristics of the original cooling load series and
displays superior capacity for cooling load forecasting, providing a basis for the optimal control strategy of air conditioning
energy saving. |
|
|
|
|
|