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Solar Hot Water (SHW) systems are a sustainable and renewable alternative for domestic and low-temperature industrial applications. As solar energy is a variable resource, performance prediction methods are useful tools to increase the overall availability and effective use of these systems. Recently, data-driven techniques have been successfully used for Prognosis and Health Management applications. In the present work, Deep Learning models are trained to predict the performance of an SHW system under different meteorological conditions. Techniques such as artificial neural networks (ANN) recurrent neural networks (RNN) and long short-term memory (LSTM) are explored. A physical simulation model is developed in TRNSYS software to generate large quantities of synthetic operational data in nominal conditions. Although similar results are achieved with the tested architectures, both RNN and LSTM …