Global approach test improvement using a neural network model identification to characterise solar combisystem performances

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• 作者：Antoine Leconte^a ; ^b ; ^{antoine.leconte@cea.fr} ; Gilbert Achard^a ; Philippe Papillon^b
• 关键词：ANN ; Artificial Neural Network ; CTSS ; Component Testing &ndash ; System Simulation ; DHW ; Domestic Hot Water ; FSC ; Fractional Solar Consumption ; SCS ; Solar CombiSystem ; SCSPT ; Short Cycle System Performance Test ; SFH ; Single Family House
• 刊名：Solar Energy
• 出版年：2012
• 期刊代码：80_0038092x
• 类别：ce
• 出版时间：July, 2012
• 卷：86
• 期：7
• 页码：2001-2016
• 文件大小：1287 K

摘要

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 鈥済rey box鈥?model, mixing a 鈥淲hite Box鈥?model composed of known physical equations and a 鈥淏lack Box鈥?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, 鈥淕rey Box鈥?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.