Modal decomposition based ensemble learning for ground source heat pump systems load forecasting

This study presents a case study of office buildings using modal decomposition based ensemble learning method to forecast energy consumption of ground source heat pump systems (GSHP). Conventional machine learning methods have uncertainty in practical application as there are lots of stochastic terms in the structure of the algorithm. Therefore, ensemble learning models are proposed to ameliorate the problem. In this paper, the prediction potential of modal decomposition based ensemble learning models are investigated while providing a comprehensive comparison on different single models and ensemble learning models in the building field. Results show that the proposed VMD based ensemble learning models have remarkable advantages in GSHP energy consumption prediction comparing with other machine learning methods, and the prediction performances of VMD based ensemble learning models measured by RMSE and MAE are 30-50% better than common machine learning methods. This work is enlightening and indicates that VMD based ensemble learning models fit well with energy consumption prediction, which could bring more efficient and concise solutions for GSHP energy consumption predictions. (C) 2019 Elsevier B.V. All rights reserved.