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

•We proposed a novel modal decomposition based ensemble learning method for GSHP energy consumption prediction.•Variational mode decomposition and empirical mode decomposition were used to decompose the energy consumption data.•The method was compared with two common used machine learning models wit...

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Bibliographic Details
Published in:Energy and buildings 2019-07, Vol.194, p.62-74
Main Authors: Xu, Chengliang, Chen, Huanxin, Xun, Weide, Zhou, Zhenxin, Liu, Tao, Zeng, Yuke, Ahmad, Tanveer
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Case studies
Decomposition
Empirical mode decomposition
Energy consumption
Ensemble learning
Ensemble model
GSHP energy consumption prediction
Heat exchangers
Heat pumps
Learning algorithms
Machine learning
Mathematical models
Office buildings
Stochasticity
Variational mode decomposition
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Summary:•We proposed a novel modal decomposition based ensemble learning method for GSHP energy consumption prediction.•Variational mode decomposition and empirical mode decomposition were used to decompose the energy consumption data.•The method was compared with two common used machine learning models with optimization process.•The method showed outstanding effect on improving the performance of GSHP energy consumption prediction. 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.
ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2019.04.018