Validation of the numerical model of a turnkey solar combi + system

D’Antoni, Matteo; Ferruzzi, Gabriele; Bettoni, Davide; Fedrizzi, Roberto

doi:10.1016/j.egypro.2012.11.065

Cited by 7 publications

(3 citation statements)

References 4 publications

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“…• the simulation of their operation, based on specific operation algorithms [32,33] • validation of their performance based on the comparison of both a simulation approach and experimental measurements of the system's operation [34] • investigation of alternative thermal energy storage technologies, involving phase change materials [35,36] • evaluation of solar-combi systems layouts involving seasonal thermal storage [23,37,38] • evaluation of solar-combi systems performance under different operation conditions [39].…”

Section: Solar-combi Systemsmentioning

confidence: 99%

Optimized Dimensioning and Operation Automation for a Solar-Combi System for Indoor Space Heating. A Case Study for a School Building in Crete

Katsaprakakis

Zidianakis

2019

Energies

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This article investigates the introduction of hybrid power plants for thermal energy production for the indoor space heating loads coverage. The plant consists of flat plate solar collectors with selective coating, water tanks as thermal energy storage and a biomass heater. A new operation algorithm is applied, maximizing the exploitation of the available thermal energy storage capacity and, eventually, the thermal power production from the solar collectors. An automation system is also designed and proposed for the realization of the newly introduced algorithm. The solar-combi system is computationally simulated, using annual time series of average hourly steps. A dimensioning optimization process is proposed, using as criterion the minimization of the thermal energy production levelized cost. The overall approach is validated on a school building with 1000 m2 of covered area, located in the hinterland of the island of Crete. It is seen that, given the high available solar radiation in the specific area, the proposed solar-combi system can guarantee the 100% annual heating load coverage of the examined building, with an annual contribution from the solar collectors higher than 45%. The annually average thermal power production levelized cost is calculated at 0.15 €/kWhth.

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Section: Solar-combi Systemsmentioning

confidence: 99%

Optimized Dimensioning and Operation Automation for a Solar-Combi System for Indoor Space Heating. A Case Study for a School Building in Crete

Katsaprakakis

Zidianakis

2019

Energies

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“…A Trnsys model of such system was developed to understand the effect of different control strategies. The design and components sizing was made starting from the thermal storage because the numerical model of this component [70] was validated using monitoring data from a SolarCombi+ installation [71] following the procedure reported in Reference [72].…”

Section: System Descriptionmentioning

confidence: 99%

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

Bettoni

Soppelsa

Fedrizzi

et al. 2019

ASI

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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.

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“…The TRNSYS program (Klein et al, 2006) has been chosen as simulation platform and the model of the system has been developed using its component library. In particular the model of the thermal storage (Drück, H., 2006) had already been validated previously using monitoring data from a SolarCombi+ installation (D'Antoni et al, 2011) and following the procedure reported in (D'Antoni M. et al, 2012). Finally, the source of the climatic data used to perform the simulations is the Meteonorm database.…”

Section: Reference Systemmentioning

confidence: 99%

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

Soppelsa¹,

Bettoni²,

Fedrizzi³

2015

Proceedings of the EuroSun 2014 Conference

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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.

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Validation of the numerical model of a turnkey solar combi + system

Cited by 7 publications

References 4 publications

Optimized Dimensioning and Operation Automation for a Solar-Combi System for Indoor Space Heating. A Case Study for a School Building in Crete

Optimized Dimensioning and Operation Automation for a Solar-Combi System for Indoor Space Heating. A Case Study for a School Building in Crete

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

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

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