Artificial Neural Network Analysis of the Solar-Assisted Heat Pumps Performance for Domestic Hot Water Production

  • Alireza Zendehboudi
  • Xianting LiEmail author
  • Siyuan Ran
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)


The solar-assisted heat pump devices have attracted ample attention as potential alternatives to the conventional technologies for domestic hot water production. Precise COP estimation of the hybrid system is a prerequisite prior to conducting any effort to design and install as well as a necessity for market support and optimal system assurance. Here, to develop a model along that has a high precision and reliability, but less complexity and computational time, the ability of soft computing approaches for predicting the performance of a solar-assisted heat pump system for domestic hot water production is reported. The experimental data gathered from a real project in China and different intelligent models are developed based on the measurements. First, owing to the complexity of our studied system and the high number of influential parameters, a novel and unique multi-objective optimization technique based on NSGA-II optimization method is proposed to determine a set of optimum variables with the highest influence on the desired output. Based on the Pareto frontier, seven input variables out of forty eight are considered for the desired output. The reliability of the developed models is evaluated via statistical and graphical error analyses. It was inferred that integration of the MLP-ANN with the suggested variable selectin algorithm outperformed the other methods by introducing an R2 = 0.9951 and RMSE = 0.0917. The current investigation can aid as a gear in the direction of improving the precision in estimation of performance of solar-assisted heat pump devices.


Domestic hot water system Solar-assisted heat pump Artificial neural network Estimation 



This study was supported by the Innovative Research Groups of the National Natural Science Foundation of China (Grant No. 51521005).


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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Building Science, School of ArchitectureTsinghua UniversityBeijingChina

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