A novel hybrid model for building heat load forecasting based on multivariate Empirical modal decomposition

Abstract

Accurate heat load forecasting is crucial for the high precise real-time operational control of buildings in winter. The inconsistency of frequencies between features and heat load, however, constrains the improvement of the ultra-short-term forecasting accuracy of heat load. This study proposed a novel hybrid model built upon Multivariate Empirical Mode Decomposition (MEMD) and Support Vector Regression (SVR) with hyper-parameters optimized by Particle Swarm Optimization (PSO), which is able to improve forecasting accuracy significantly. Meanwhile, Sliding Window (SW) is employed to overcome the limitations of MEMD in forecasting, and feature selection is carried out using eXtreme Gradient Boosting (XGBoost) before modeling to minimize errors and reduce the workload. The principle of the proposed SW-MEMD-PSO-SVR hybrid model is to decompose the associated features and heat load into several groups of components by MEMD, maintaining a constant frequency of feature components and heat load components for each forecasting. A real building in Inner Mongolia, China, have been considered as an example to verify the superiority of the proposed hybrid model. The proposed SW-MEMD-PSO-SVR hybrid model has the best values of MAPE, NMBE, CVRMSE and R2, being 2.68%, 0.09%, 3.52%, and 84.90%, respectively. The results demonstrated that the proposed hybrid model is a promising alternative for improving accuracy of ultra-short-term building heat load forecasting.