Artificial Neural Network modelling of sorption chillers

作者：Frey Patrick^*; Fischer Stephan; Drueck Harald

来源：Solar Energy, 2014, 108: 525-537.

DOI：10.1016/j.solener.2014.08.006

• CTSS method
• EN 12977
• Solar thermal cooling
• Sorption chiller
• Artificial Neural Network
• Performance testing

Solar cooling is still a young and small but growing market with a large potential. An increasing market development of solar cooling and so-called SolarCombiPlus systems (solar thermal systems providing domestic hot water, space heating and space cooling) can help to reduce primary energy demand and hence emissions of greenhouse gases. To support the market entry and to enhance the market penetration it is important to strengthen consumers%26apos; confidence in these systems. An important aspect for achieving this goal is a standardised method to predict the performance of the complete solar cooling system under real operating conditions. Nonetheless, objective performance test methods are not yet common standard. In this context a performance test method for solar cooling and SolarCombiPlus systems based on the CTSS method (Component Testing System Simulation) has been developed by the Research and Testing Centre for Thermal Solar Systems (TZS) of the Institute for Thermodynamics and Thermal Engineering (ITW) at the University of Stuttgart within the project %26quot;SolTrans%26quot;. For the proposed extended CTSS method numerical models are required in order to describe the thermal behaviour of sorption chillers. The main target of the work presented in this paper is dedicated to the development of appropriate models for sorption chillers which can be used for the extended CTSS method. The approach used is the experimental system identification(1) based on Artificial Neural Networks (ANN). In this approach experimentally measured data are used to derive an ANN model which is able to predict the outlet temperatures of a sorption chiller. In the work presented, measured data of an adsorption chiller were used to develop such an ANN model which is suitable to predict the outlet temperatures of the three hydraulic loops of adsorption chillers. The model was validated with measured data obtained under real operating conditions. The simulated output temperatures show a very good agreement with the measured temperatures.

• 出版日期2014-10