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Open AccessFeature PaperArticle

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

1
Eurac Research, Viale Druso 1, 39100 Bolzano, Italy
2
Universidad Politecnica de Madrid, Calle Ramiro de Maeztu 7, 28040 Madrid, Spain
*
Author to whom correspondence should be addressed.
Appl. Syst. Innov. 2019, 2(2), 15; https://doi.org/10.3390/asi2020015
Received: 8 January 2019 / Revised: 11 April 2019 / Accepted: 19 April 2019 / Published: 25 April 2019
(This article belongs to the Special Issue Solar Thermal Systems)
This paper discusses the development of a coupled Q-learning/fuzzy control algorithm to be applied to the control of solar domestic hot water systems. The controller brings the benefit of showing performance in line with the best reference controllers without the need for devoting time to modelling and simulations to tune its parameters before deployment. The performance of the proposed control algorithm was analysed in detail concerning the input membership function defining the fuzzy controller. The algorithm was compared to four standard reference control cases using three performance figures: the seasonal performance factor of the solar collectors, the seasonal performance factor of the system and the number of on/off cycles of the primary circulator. The work shows that the reinforced learning controller can find the best performing fuzzy controller within a family of controllers. It also shows how to increase the speed of the learning process by loading the controller with partial pre-existing information. The new controller performed significantly better than the best reference case with regard to the collectors’ performance factor (between 15% and 115%), and at the same time, to the number of on/off cycles of the primary circulator (1.2 per day down from 30 per day). Regarding the domestic hot water performance factor, the new controller performed about 11% worse than the best reference controller but greatly improved its on/off cycle figure (425 from 11,046). The decrease in performance was due to the choice of reward function, which was not selected for that purpose and it was blind to some of the factors influencing the system performance factor. View Full-Text
Keywords: domestic hot water systems; fuzzy control; reinforced learning; simulations; solar domestic hot water systems; fuzzy control; reinforced learning; simulations; solar
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Bettoni, D.; Soppelsa, A.; Fedrizzi, R.; del Toro Matamoros, R.M. Analysis and Adaptation of Q-Learning Algorithm to Expert Controls of a Solar Domestic Hot Water System. Appl. Syst. Innov. 2019, 2, 15.

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