山东电力技术2026，Vol.53Issue(8)：100-110,11.DOI:10.20097/j.cnki.issn1007-9904.250556
基于SHAP的空调负荷预测模型特征标签构建方法
A Feature Label Construction Approach for Air Conditioning Load Prediction Models Using SHAP Value Analysis
摘要
Abstract
A feature label construction approach is introduced,leveraging SHAP(shapley additive explanations)to tackle the challenges of complex influencing factors and difficult feature selection in building air conditioning load prediction.This method adopts the principle of"model first,filter later"and proposes a SHAP-based feature label construction framework grounded in the long short-term memory with temporal attention mechanism(LSTM-TAM)air conditioning load prediction model.It quantifies the contribution of each input feature to the prediction outcomes,enabling the identification of key features and the establishment of a label system through threshold-based screening and retraining verification.Taking office buildings in Zhenjiang as a case study,this research analyzes the distribution of SHAP values and constructs a feature label optimization model.The results demonstrate that the feature labels developed through this method significantly enhance the accuracy and time efficiency of air conditioning load prediction.Specifically,the approach markedly reduces the root mean square error(RMSE)and mean absolute error(MAE),increases the coefficient of determination(R2)by over 10%,and cuts training time by 40%.关键词
特征标签/SHAP/溯源分析/长短期记忆/时序注意力Key words
feature label/SHAP/traceability analysis/long short-term memory/temporal attention分类
信息技术与安全科学引用本文复制引用
任禹丞,王雨薇,郑杨,胡涵天,邵平..基于SHAP的空调负荷预测模型特征标签构建方法[J].山东电力技术,2026,53(8):100-110,11.基金项目
国家电网有限公司科技项目"基于关口量测的典型公共建筑空调负荷分解及节电潜力评估技术研究与应用"(5400-202318574A-3-2-ZN). Science and Technology Foundation of State Grid Co.,Ltd."Research and Application of Load Decomposition and Energy-saving Potential Assessment for Air Conditioning Load Based on Gate Measurement in Typical Public Buildings"(5400-202318574A-3-2-ZN). （5400-202318574A-3-2-ZN）
