Performance Prediction and Optimization of Solar Water Heater via a Knowledge-Based Machine Learning Method
This work addresses the time and cost inefficiencies in measuring solar energy systems for engineers and researchers, but it is incremental as it applies existing machine learning models to a specific domain.
The authors tackled the challenge of predicting and optimizing solar water heater performance by using a knowledge-based machine learning method, achieving precise predictions and proposing a high-throughput screening strategy for optimization.
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.