基于相似日和Optuna-LightGBM的智能控制柜内部环境预警评估方法  

Early-Warning Evaluation Method of Intelligent Control Cabinet Interior Environment Based on Similar Days and Optuna-LightGBM

作　　者：尹康 钟婷婷 黄昕颖 李丽 YIN Kang;ZHONG Tingting;HUANG Xinying;LI Li(Zhejiang Huayun Electric Power Engineering Design Consulting Co.,Ltd.,Hangzhou 310000,China)

机构地区：[1]浙江华云电力工程设计咨询有限公司,浙江杭州310000

出　　处：《电器与能效管理技术》2023年第7期70-76,共7页Electrical & Energy Management Technology

摘　　要：针对基于事件驱动的智能控制柜温湿度预测精度较低,无法及时对柜内温湿度异常进行预警的问题,提出了一种基于相似日和Optuna-LightGBM的温湿度预测方法。利用相似日算法选取合适的模型训练数据集,构建基于LightGBM的温湿度预测模型,用Optuna优化模型参数。最后,提出了一种基于曲线拐点检测的预警参数阈值计算方法,分析预测模型得到的温湿度曲线特性,实现温湿度预警。实验结果显示,所提方法的温度预测误差MAPE为0.35%,湿度预测误差MAPE为0.73%,可实现对柜内温湿度的精准预测并及时预警。A temperature and humidity prediction method based on similar days and Optuna-LightGBM is proposed to address the issue of low prediction accuracy of temperature and humidity in event driven intelligent control cabinets,which can’t provide the timely warning of abnormal temperature and humidity inside the cabinets.The similar day algorithm is used to select the suitable model training data sets.A temperature and humidity prediction model is constructed based on LightGBM.Using Optuna the model parameters are optimized.Finally,a threshold calculation method for warning parameters based on the curve inflection point detection is proposed.The temperature and humidity curve characteristics obtained from the prediction model are analyzed to achieve temperature and humidity warning.The experimental results show that the temperature prediction error MAPE of the proposed method is 0.35%,and the humidity prediction error MAPE is 0.73%,which can achieve the accurate prediction of temperature and humidity inside the cabinet and timely warning.

关 键 词：轻量级梯度提升机 Optuna 相似日算法 环境预警 温湿度控制系统 

分 类 号：TM93[电气工程—电力电子与电力传动]