• Energy Research

AbstractThe development of a small capacity absorption chiller and the numerical and experimental results are presented in this paper. The prototype is a thermally driven ammonia-water absorption chiller of 5kW cooling capacity for solar cooling applications. The chiller was developed in an industrial perspective with a goal of overall compactness and using commercially available components. In order to characterize various component technologies and different optimization components, the prototype is monitored with temperature, pressure and mass flow rate accurate sensors. The resulting chiller, characterized by a reduced load in ammonia-water solution and the use of brazed plate heat exchanger, has shown good performance during the preliminary tests. A comparison with the expected numerical results is given.

Abstract The aim of this paper is to present a methodology to model and evaluate the energy performance and outlet temperatures of absorption chillers so that users can have reliable information on the long-term performance of their systems in the desired boundary conditions before the product is installed. Absorption chillers' behaviour could be very complex and unpredictable, especially when the boundary conditions are variable. The system dynamic must therefore be included in the model. Artificial neural networks (ANNs) have proved to be suitable for handling such complex problems, particularly when the physical phenomena inside the system are difficult to model. Reliable “black box” ANN modelling is able to identify the system's global model without any advanced knowledge of its internal operating principles. Knowledge of the system's global inputs and outputs is sufficient. The methodology proposed was applied to evaluate a commercial absorption chiller. Predictions of the ANN model developed were compared, with a satisfactory degree of precision, to 2 days of experimental measures. These days were chosen to be representative of the real dynamic operating conditions of an absorption chiller. The neural model predictions are very satisfactory: absolute relative errors of the transferred energy are within 0.1–6.6%.

Abstract Solar CombiSystems (SCSs) are very efficient systems for reducing conventional energy consumption of building but their thermal performances are strongly dependent on the environment where they are installed (type of climate and thermal quality of the building). Currently it is impossible to predict the energy savings generated by a SCS as there is no standard test to characterise SCS performances. Currently, the Short Cycle System Performance Test (SCSPT), based on a 12 days test of the complete SCS on a semi-virtual test bench, is able to predict annual energy savings with a good accuracy, but the performance prediction is limited to only one environment (the building and the climate corresponding with the test). Based on the SCSPT procedure, this paper proposes an improvement of the method by identifying a global SCS model from the test data. Then, the identified model would be able to simulate the tested SCS in any environment and thus to characterise its performances. The proposed model to identify is a “grey box” model, mixing a “White Box” model composed of known physical equations and a “Black Box” model, which is an Artificial Neural Network (ANN). A complete process is developed to train and select a relevant global SCS model from such a test. This approach has been validated through numerical simulations of three detailed SCS models. Compared to those annual results, “Grey Box” SCS models trained from a twelve days sequence are able to predict energy consumption with a good accuracy for 27 different environments. An experimental application of this procedure has been used to characterise a real system.

AbstractThe purpose of this study is the seasonal storage of solar thermal energy for household applications. Thermochemical storage has been adopted with the use of moist air for hydration/dehydration. Various aeraulic processes have been evaluated and compared. Parametric studies enable to have more information concerning the influence of external conditions, component performances and salt characteristics.This study points out that thermochemical storage conception has to be studied has a whole: material, reactor and process.It also provides ways to optimize thermochemical heat storage depending on outdoor conditions and objectives in term of performances.

AbstractThe development of solar district heating is gaining more and more interest, but, in some case the space available for the integration of solar collectors on the ground is limited and the use of decentralized systems is necessary.For decentralized solar district heating systems different hydraulic schemes at the substation level, with or without local use of solar energy, are possible. The present paper detailed an advanced study on decentralized solar district heating system using dynamic simulation software. Nine different hydraulic schemes for substations have been investigated with a return to return feed in. For each scheme many parameters that influence the performance of the solar installation have been studied such as the district heating network return temperature, the solar collector area and the type of solar collector (low temperature or high temperature solar collector). The comparison between the different hydraulic schemes is based on thermal efficiency but also on solar energy cost using the methodology of the Levelized Cost Of Energy (LCOE).

Abstract At present there is no reliable approach to model and characterize thermal systems using renewable energy for building applications based on experimental data. The results of the existing approaches are valid only for specific conditions (climate type and thermal building properties). The aim of this paper is to present a generic methodology to evaluate the energy performance of such systems. Artificial neural networks (ANNs) have proved to be suitable to tackle such complex problems, particularly when the system to be modelled is compact and cannot be divided up during the testing stage. Reliable “black box” ANN modelling is able to identify global models of the whole system without any advanced knowledge of its internal operating principles. The knowledge of the system’s global inputs and outputs is sufficient. The proposed methodology is applied to evaluate three different Solar Combisystems (SCSs) combined with a gas boiler or a heat pump (HP) as an auxiliary system. The results show that the best ANN models were able to predict with a satisfactory degree of precision, the annual energy consumption of the all systems except the SCS combined with air source HP, in different conditions, based on a learning sequence lasting only 12 days. In fact, the annual energy prediction errors were less than 10% in most cases. The methodology limitations appear in extreme boundary conditions (Barcelona climate) compared to those used during the ANN training process.