Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method

TL;DR

This paper presents a machine learning-based approach to accurately predict and optimize solar water heater performance, reducing time and costs associated with traditional measurement methods.

Contribution

It introduces a knowledge-based machine learning framework, including ANN, SVM, and ELM models, and a high-throughput screening strategy for system optimization.

Findings

ANN outperforms other models in prediction accuracy

Developed an ANN-based software for quick performance prediction

Proposed a high-throughput screening method for system design

Abstract

Measuring the performance of solar energy and heat transfer systems requires a lot of time, economic cost and manpower. Meanwhile, directly predicting their performance is challenging due to the complicated internal structures. Fortunately, a knowledge-based machine learning method can provide a promising prediction and optimization strategy for the performance of energy systems. In this Chapter, the authors will show how they utilize the machine learning models trained from a large experimental database to perform precise prediction and optimization on a solar water heater (SWH) system. A new energy system optimization strategy based on a high-throughput screening (HTS) process is proposed. This Chapter consists of: i) Comparative studies on varieties of machine learning models (artificial neural networks (ANNs), support vector machine (SVM) and extreme learning machine (ELM)) to predict the performances of SWHs; ii) Development of an ANN-based software to assist the quick prediction and iii) Introduction of a computational HTS method to design a high-performance SWH system.