Performance of a Coupled Reinforced Learning-Fuzzy Control Approach to the Control of a Solar Domestic Hot Water System

Abstract

The paper discusses the results of a comparative analysis of the performance of different control strategies applied to a reference solar DHW system. Three classes of control strategies have been considered: so-called naïve control strategies based on the on-off control of the solar collectors pump using temperatures difference, solar irradiation or both; fuzzy-based control strategies; and a reinforced learning-based strategy coupling a Qlearning algorithm to a fuzzy controller. The performance figures used in the analysis are the seasonal performance factor at the primary side of the circuit (SPFcoll), the seasonal performance factor of the whole DHW preparation process (SPFDHW) and the number of times the circulation pump is switched on and off (NON-OFF). The analysis, carried out numerically, has been performed using the TRNSYS simulation software coupled to a LabVIEW implementation of the controllers. The analysis suggests that controllers able to find a nearly optimal policy without requiring prior modelling of the system can be implemented using a reinforced learning algorithm and supports the fact that well designed control strategies can increase significantly the performance of such systems.

Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System

“…The performance of this coupled RL/FC controller has been compared to other control practices, such as traditional on/off, proportional feedback control and FC. The original work presented in Reference [5] is here extended by providing an extended literature review, a detailed description of the algorithm used, and additional results about the effect of membership functions and the impact of RL parameters.…”