Short-term Building Heating Load Prediction Based On Machine Learning

According to data from the International Energy Agency in 2019,the construction industry accounts for more than the replacement of global final energy consumption,accounting for nearly 40% of total direct and indirect carbon dioxide emissions.In the final energy use of buildings,the heating load accounts for a large proportion The short-term load forecasting of buildings is a promising method for building energy management optimization.The currently used building forecasting methods are based on physical principles,but the construction of building models requires a lot of manpower and time,which limits the This method is popularized on a large scale.There is a huge space left for using machine learning to predict building heating loads.In order to obtain data about building machine learning building heating load prediction models,EnergyPlus was used to build the building model.The Pearson correlation coefficient method was used to select the variables related to the building heating load.The load-related variables and their past 24 hours data were selected as the original training data set,and statistical methods were used.Extract the input features of machine learning building heating load prediction model from the original data set,and use unsupervised machine learning algorithms to extract features.Discuss the potential of machine learning to extract load-related features.The input features extracted by statistical methods and unsupervised machine learning algorithms are used to establish a short-term building heating load prediction model based on machine learning.The learning algorithm has better generalization ability for structure-based risk minimization and empirical risk minimization Support Vector Machines.Use the R2 coefficient to publish the grade prediction model accuracy.