  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.
