Generalized Prediction of Commercial Buildings Cooling and Heating Load Based on Machine Learning Technology

Abstract

Factors such as building area, materials, light transmittance, and housing orientation have significant effects on cold and heat loads of commercial buildings. Accurate prediction of cold and heat loads based on the characteristics of the building can provide a reliable data basis for power grid dispatch. In order to quantitatively study the influence of the commercial building factors on the cooling and heating load, this paper collected the load of 768 buildings in a certain area on a typical day in spring and the relative density, surface area, room orientation, and light transmission area of the building group. This paper uses typical machine learning methods such as random forest, extra random tree, bagging, and deep neural network to perform regression modeling on the data. Finally, three models with the best effects are selected for blending to obtain the final building cooling and heating load prediction model. In preset 156 test cases, the mean square error of the model for heat load prediction is 0.201 and the mean square error of cooling load forecast is 2.56. The blending model reduces the error to a smaller range and reduces the phenomenon of data overfitting.