Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model

Zhaoyi Zhuang; Xinliang Zhai; Xianye Ben; Bin Wang; Dijia Yuan

doi:10.7717/peerj-cs.482

Accurately predicting heat transfer performance of ground-coupled heat pump system using improved autoregressive model

PeerJ Comput Sci. 2021 Apr 20:7:e482. doi: 10.7717/peerj-cs.482. eCollection 2021.

Authors

Zhaoyi Zhuang¹, Xinliang Zhai², Xianye Ben³, Bin Wang¹, Dijia Yuan³

Affiliations

¹ College of Thermal Energy Engineering, Shandong Jianzhu University, Jinan, China.
² School of Electronic lnformation and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
³ School of Information Science and Engineering, Shandong University, Jinan, China.

Abstract

Nowadays, ground-coupled heat pump system (GCHP) becomes one of the most energy-efficient systems in heating, cooling and hot water supply. However, it remains challenging to accurately predict thermal energy conversion, and the numerical calculation methods are too complicated. First, according to seasonality, this paper analyzes four variables, including the power consumption of heat pump, the power consumption of system, the ratios of the heating capacity (or the refrigerating capacity) of heat pump to the operating powers of heat pump and to the total system, respectively. Then, heat transfer performance of GCHP by historical data and working parameters is predicted by using random forests algorithm based on autoregressive model and introducing working parameters. Finally, we conduct experiments on 360-months (30-years) data generated by GCHP software. Among them, the first 300 months of data are used for training the model, and the last 60 months of data are used for prediction. Benefitting from the working condition inputs it contained, our model achieves lower Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) than Exponential Smoothing (ES), Autoregressive Model (AR), Autoregressive Moving Average Model (ARMA) and Auto-regressive Integrated Moving Average Model (ARIMA) without working condition inputs.

Keywords: Autoregressive model with working condition inputs; Ground-coupled heat pump system; Random forests; Time series analysis.

Grants and funding

This work is supported by the National Key Research and Development Program of China (Grant No. 2020YFC0833303), the Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) (Grant No. 2019JZZY010119), the National Natural Science Foundation of China (Grant No. 51708339, 61971468, 51808321), the Leading Researcher Studio Fund of Jinan (Grant No. 2019GXRC066), the Scientific, Technological Innovation Project for Youth of Shandong Provincial Colleges and Universities (Grant No. 2019KJH012) and Science and Technology Innovation & Breakthrough Plan of Heze (KJCXTP202006). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.