Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the genetic algorithm (GA), and a GA-SVM prediction model was proposed to perform building hourly cold load prediction. Then, when there is a demand for the fluctuation prediction of BCL or extreme weather conditions are encountered, the information granulation (IG) method is used to fuzzy granulate the data. At the same time, the fluctuation range of the BCL was obtained by combining the prediction of the established GA-SVM model. Finally, the model was validated with the actual operational data of a large public building in Xi'an. The results show that the CV-RMSE and MAPE of the GA-SVM model are reduced by 58.85% and 68.04%, respectively, compared with the SVM for the time-by-time BCL prediction, indicating that the optimization of the SVM by using the GA can effectively reduce the error of the prediction model. Compared with the other three widely used prediction models, the R-2 of the GA-SVM model is improved by 4.75 similar to 6.35%, the MAPE is reduced by 68.00 similar to 72.76%, and the CV-RMSE is reduced by 59.69 similar to 64.97%. This proved that the GA-SVM has higher prediction accuracy. In addition, the joint model was used for BCL fluctuation range prediction, and the R2 of the prediction model was 97.27 similar to 99.68%, the MAPE was 2.59 similar to 2.84%, and the CV-RMSE was only 0.0249 similar to 0.0319, which demonstrated the effectiveness of the joint prediction model. The results of the study have important guiding significance for building load interval prediction, daily energy management and energy scheduling.
