Artificial neural networks for the performance prediction of large solar systems
Artificial neural networks for the performance prediction of large solar systems
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DOI：
10.1016/j.renene.2013.08.049
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发表时间：
2014-03
期刊：
影响因子：
8.7
通讯作者：
S. Kalogirou;E. Mathioulakis;V. Belessiotis
中科院分区：
文献类型：
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作者：
S. Kalogirou;E. Mathioulakis;V. Belessiotis
本文将人工神经网络（ann）用于大型太阳能系统的性能预测。利用人工神经网络方法预测典型工况下的日预期发电量，以及每日运行周期结束时储罐能达到的温度水平。这些被认为是用户最重要的参数。近一年（226天）的实验测量已被用于研究人工神经网络模拟典型大型太阳系能量行为的能力。结果表明，人工神经网络可以有效地预测系统的日常能源性能；训练数据集和验证数据集的统计r2值分别优于0.95和0.96。验证中使用的数据对人工神经网络来说是完全未知的，这证明了人工神经网络对完全未知的数据有很好的预测能力。将该方法得到的结果与输入-输出模型预测结果进行了比较，精度较高，而多元线性回归不能给出准确的结果。此外，将该网络用于各种输入参数组合，得到的结果与所提方法相同数量级，证明了该方法的鲁棒性。所提出的方法的优点包括实现简单，即使在系统组件的特性未知的情况下，以及通过在系统运行期间不断添加收集的新数据来提高人工神经网络预测太阳能系统性能的能力的潜力。
In this paper, artificial neural networks (ANNs) are used for the performance prediction of large solar systems. The ANN method is used to predict the expected daily energy output for typical operating conditions, as well as the temperature level the storage tank can reach by the end of the daily operation cycle. These are considered as the most important parameters for the user. Experimental measurements from almost one year (226 days) have been used to investigate the ability of ANN to model the energy behavior of a typical large solar system. From the results, it can be concluded that the ANN effectively predicts the daily energy performance of the system; the statisticalR2-value obtained for the training and validation data sets was better than 0.95 and 0.96 for the two performance parameters respectively. The data used in the validation were completely unknown to the ANN, which proves the ability of the ANN to give good predictions on completely unknown data. The results obtained from the method were also compared to the input–output model predictions with good accuracy whereas multiple linear regression could not give as accurate results. Additionally, the network was used with various combinations of input parameters and gave results of the same order of magnitude as the suggested method, which prove the robustness of the method. The advantages of the proposed approach include the simplicity in the implementation, even when the characteristics of the system components are not known, as well as the potential to improve the capability of the ANN to predict the performance of the solar system, through the continuous addition of new data collected during the operation of the system.