Lighting load prediction study based on LightGBM-GA-Seq2Seq
Lighting load is an important part of building energy consumption,and under the dual requirements of improving lighting quality and reducing lighting energy consumption,the criticality of lighting load prediction has gradually come to the fore.With the development of information technology,the increasing improvement of load data collection platform makes the scale of load data expanding,and also accompanied by data complexity.In order to further predict the lighting load accurately,it is more and more important to analyze and obtain the behavioral patterns of the load,based on which this study proposes an integrated learning lighting load prediction method.The pattern labels of the predicted day are analyzed and acquired by K-means and Light GradientBoostingMachine(LightGBM)algorithms,and together with the lighting loads and climate characteristics,they are used as inputs to the Sequenceto Sequence(Seq2Seq)model to predict the lighting loads.By analyzing the real cases,the proposed method significantly improves the performance indexes of normalized root mean square error(NRMSE)and coefficient of determination(R2)by 18％and 8％,respectively,compared with the traditional method without behavioral pattern labels,which demonstrates the feasibility and superiority of the proposed method in the field of lighting load prediction.
lighting load predictionbehavioral patternslighting energy consumption
万方数据
维普
