Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Hao Li,Zhien Zhang,Kejun Liu,Zhijian Liu

doi:10.1155/2017/4194251

Abstract

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems.

Highlights

How to cost-effectively design a high-performance solar energy conversion system has long been a challenge
Kalogirou et al have done a large number of machine learning-based numerical predictions of some important coefficients of thermal performance (CTP) for solar energy systems [12,13,14,15,16,17,18,19]. Their results show that there is a huge potential application of machine learning techniques to energy systems. Based on their successful works, we recently developed a series of machine learning models for the predictions of heat collection rates and heat loss coefficients (the average heat loss per unit, W/(m3K)) to a water-inglass evacuated tube solar water heater (WGET-Solar water heater (SWH)) system [2, 20, 21]
Our results show that with some easymeasured independent variables, both heat collection rates and heat loss coefficients can be precisely predicted after some proper trainings from the datasets, with proper algorithms (e.g., artificial neural networks (ANNs) [2, 20], support vector machine (SVM) [2], and extreme learning machine (ELM) [21])

Summary

Introduction

How to cost-effectively design a high-performance solar energy conversion system has long been a challenge. To the best of our knowledge, very few references concern about the optimization of thermal performance of energy systems using such a powerful knowledge-based technique [22] To address this challenge, we recently used a high-throughput screening (HTS) method combined with a well-trained ANN model to screen 3.5 × 108 possible designs of new WGET-SWH settings, in good agreement with the subsequent experimental validations [23]. Since tube solar collectors have a substantially lower heat loss coefficient than other types of collectors [12, 32], WGET-SWHs gradually become popular during the past decades [33,34,35], with the advantages of excellent thermal performance and easy transportability [36, 37] With this reason, we chose the WGET-SWH system as a typical SWH, to show how a well-developed ANN model can be used to cost-effectively optimize the thermal performance of an SWH system, using an HTS method

Machine Learning Methods

HTS-Based Optimization Framework

Design B

Conclusions

Findings

Conflicts of Interest