Weighted residual clustering-based building load prediction interval estimation

doi:10.3785/j.issn.1008-973X.2022.05.010

Journal of ZheJiang University (Engineering Science)

2022, Vol. 56

Issue (5): 930-937 DOI: 10.3785/j.issn.1008-973X.2022.05.010

Weighted residual clustering-based building load prediction interval estimation

Chao-bo ZHANG1,2,3(

),Yong-zheng LIU4,Hong-bo LI1,2,*(

),Yang ZHAO3,Li-zhu ZHANG3,Zi-hao WANG3

1. State Key Laboratory of Air-Conditioning Equipment and System Energy Conservation, Zhuhai 519000, China
2. Guangdong Key Laboratory of Refrigeration Equipment and Energy Conservation Technology, Zhuhai 519000, China
3. Institute of Refrigeration and Cryogenics, Zhejiang University, Hangzhou 310027, China
4. College of Energy Engineering, Zhejiang University, Hangzhou 310027, China

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Abstract

A weighted residual clustering-based prediction interval estimation method was proposed for quantifying uncertainties in building energy load prediction. Firstly, the Shapley additive explanations approach was introduced to calculate a contribution level of each model input to model outputs for a specific load prediction model. Then, the contribution level was adopted for weighted clustering of model inputs to obtain the distribution of historical model residuals in different clusters. Finally, load prediction intervals of the model were estimated based on the distribution of residuals in different clusters. This method was validated on the one-year cooling load data set from a public building located in Shenzhen, Guangdong, China. Results showed that this method had higher accuracy of interval estimation than conventional methods whose inputs were not weighted in residual clustering. The average coverage error of prediction intervals was 1.87% using this method, while the average coverage error of prediction intervals was 2.27% using conventional methods. This method is applicable for any data-driven building energy load prediction models. It can be utilized to provide accurate and reliable building load prediction for optimal control and fault detection.

Key words： building energy load prediction interval estimation data-driven model model interpretability residual clustering

Received: 15 June 2021 Published: 31 May 2022

CLC:

TU 831.2

Fund: 国家自然科学基金资助项目（51978601）；空调设备与系统节能国家重点实验室资助项目（ACSKL2019KT07）

Corresponding Authors: Hong-bo LI E-mail: chaoboo.zhang@zju.edu.cn;lihongbo@cn.gree.com

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Cite this article:

Chao-bo ZHANG,Yong-zheng LIU,Hong-bo LI,Yang ZHAO,Li-zhu ZHANG,Zi-hao WANG. Weighted residual clustering-based building load prediction interval estimation. Journal of ZheJiang University (Engineering Science), 2022, 56(5): 930-937.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2022.05.010 OR https://www.zjujournals.com/eng/Y2022/V56/I5/930

基于加权残差聚类的建筑负荷预测区间估计

提出基于加权残差聚类的建筑负荷预测区间估计方法，旨在对建筑负荷预测模型的不确定性进行定量评估. 使用Shapley additive explanations方法量化负荷预测模型的每个输入对输出的贡献程度. 基于得到的贡献程度对模型输入进行加权聚类，获得不同聚类簇中的模型历史残差分布. 根据不同聚类簇中的残差分布估计模型的预测区间. 在深圳某办公建筑1 a的冷负荷数据集上进行验证. 结果表明，与传统不对输入进行加权的方法相比，该方法可以显著提高预测区间的估计精度. 期望得到的预测区间与该方法得到的预测区间的平均覆盖误差为1.87%，而传统方法的平均覆盖误差为2.27%. 该方法可以用于估计任何数据驱动的建筑负荷预测模型的不确定性，从而为优化控制和故障诊断提供更可靠的负荷预测模型.

关键词： 建筑负荷预测, 区间估计, 数据驱动模型, 模型可解释性, 残差聚类

Fig.1 Flowchart of prediction interval estimation method

Tab.1 Hyper-parameter optimization results of XGBoost

Tab.2 Accuracy of XGBoost model on training and testing sets　　　　　　　　　　

Fig.2 Violin plots of local SHAP values for each model input

Tab.3 Normalized global SHAP values and XGBoost’s feature importance for each model input

Tab.4 Mean of absolute values of ACE under different k

Fig.3 Residual distributions of clusters with k of three

Tab.5 Performance of prediction interval estimation under different PINC (k = 3)

Fig.4 Illustration of actual load curve, predicted load curve and estimated prediction intervals on a typical day (PINC=80% and 90%)


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