Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps
Artificial neural network modeling of a photovoltaic-thermal evaporator of solar assisted heat pumps
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DOI：
10.1016/j.energy.2015.09.078
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发表时间：
2015-12
期刊：
影响因子：
9
通讯作者：
N. Gunasekar;M. Mohanraj;V. Velmurugan
中科院分区：
文献类型：
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作者：
N. Gunasekar;M. Mohanraj;V. Velmurugan
在这项工作中，人工神经网络模型被开发用于预测用于太阳能热泵的光热蒸发器的能量性能。实验是在印度哥印拜陀市（北纬10.98°，东经76.96°）的气象条件下进行的。以太阳辐射强度、环境温度、环境风速和环境相对湿度4个环境参数为参考，观测了光热蒸发器的能量性能参数，如蒸发器得热、太阳能输入比、光伏效率和光伏板温度。实验结果被用作网络的训练数据。将多层前馈网络优化为4-15-4结构，用于预测光热蒸发器的能量性能。进行方差分析，以确定显着的环境参数影响的光热蒸发器的能量性能。网络预测被发现是更接近实验值的最大部分的绝对方差值，最小的均方根误差和最小的方差系数值。方差分析结果证实，太阳能强度和环境温度是影响光热蒸发器能量性能的最重要参数。
In this work, the artificial neural network model was developed to predict the energy performance of a photovoltaic-thermal evaporator used in solar assisted heat pumps. The experiments were carried out under the meteorological conditions of Coimbatore city (latitude of 10.98°N and longitude of 76.96°E) in India. The energy performance parameters of a photovoltaic-thermal evaporator such as, evaporator heat gain, solar energy input ratio, photovoltaic efficiency and photovoltaic panel temperature were observed with reference to four ambient parameters such as, solar intensity, ambient temperature, ambient wind velocity and ambient relative humidity. The experimental results were used as training data for the network. The multilayer feed forward network is optimized to 4-15-4 configuration for predicting the energy performance of the photovoltaic-thermal evaporator. Analysis of variance was carried out to identify the significant ambient parameter influencing the energy performance of photovoltaic-thermal evaporators. The network predictions are found to be closer to the experimental values with the maximum fraction of absolute variance values, minimum root mean square errors and minimum coefficient of variance values. The analysis of variance results confirmed that solar intensity and ambient temperature are the most influencing parameters affecting the energy performance of photovoltaic-thermal evaporators.