基于CFD和LightGBM算法的建筑室内温度全局预测模型

Global prediction model for indoor temperature based on CFD and LightGBM algorithm

温度控制对建筑节能意义重大,室内温度准确预测是建筑温度精确控制的前提。本文提出一种基于计算流体动力学(CFD)和LightGBM算法的建筑室内全局温度预测模型实现对同一时间全局温度模拟和全局时间序列温度变化预测。基于空间建筑结构、传感器精度范围和实际温度控制范围简化的CFD简化模型满足精度要求同时解决了数据冗余的问题,更具备实践性。在此基础上通过LightGBM和LSTM算法模拟全局区域温度空间序列变化规律,采用LightGBM算法预测温度时间序列变化实现对室内温度全局预测。试验采用某地区烟草储存库全年建筑运行数据和室内外温度监测数据,构建室内全局温度预测模型,通过实际测量温度数据实验验证,建筑全局5 h温度分布预测准确系数为0.955 4,60 h温度范围预测准确系数为0.994 0,对比ANN,BP,LSTM算法,本文模型平均准确系数提高0.022 4~0.014 7。

Temperature control is significant to building energy conservation, and the accurate prediction of indoor temperature is the prerequisite for precise control of building temperature. Proposes a global indoor temperature prediction model based on computational fluid dynamics(CFD) and LightGBM algorithms to realize global temperature simulation and global temperature change prediction over time. The simplified CFD model is based on the space building structure, sensor accuracy range, and actual temperature control range, which can meet the accuracy requirements and solve data redundancy, making it more practical. On this basis, the LightGBM algorithm and LSTM algorithm are used to simulate the global temperature spatial sequence change law. To be specific, the LightGBM algorithm is employed to predict the temperature-time sequence changes to realize the global prediction of indoor temperature. The experiment utilizes the annual building operation data and indoor and outdoor temperature monitoring data of a tobacco storage warehouse to construct an indoor global temperature prediction model. Experimental results of the practical measured temperature data show that the temperature distribution accuracy coefficient of 5 h global forecast is 0.955 4, and the temperature range accuracy coefficient of 60 h global predict is 0.994 0. Compared with the ANN, BP, and LSTM algorithms, the average accuracy coefficient of the proposed model is improved by 0.022 4~0.014 7.