ESTIMASI INTENSITAS RADIASI MATAHARI DENGAN MENGGUNAKAN JARINGAN SYARAF TIRUAN BACKPRPAGATION DI KOTA JAYAPURA

Presli Panusunan Simanjuntak, Krisnandi Pandu Wibowo
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Abstract

Eastern Indonesia has enormous potential for solar energy. For the utilization of this energy, data on the intensity of solar radiation is needed that can describe the availability of solar energy that can be utilized. Information on the availability of solar energy will be used to estimate the intensity of solar radiation, so that the use of solar energy can be optimal. In this study, the intensity of solar radiation was estimated. The data used to estimate the intensity of solar radiation were air temperature, humidity, duration of solar radiation, and rainfall. The method used in this study is an artificial neural network (ANN) with backpropagation training. This study uses variations in the number of neurons in 1 hidden layer to get the best group based on the RMSE value and correlation. The best group from each training simulation is then used to estimate solar radiation. The estimation results for the city of Jayapura have an RMSE value of 1,970 kWh/m2. The solar radiation received in the Jayapura area has a high enough potential to be used as alternative energy with an average monthly radiation value of 4,5 kWh/m2.
通过在查亚普拉市使用模拟神经网络进行太阳辐射强度评估
印尼东部拥有巨大的太阳能潜力。为了利用这种能源,需要关于太阳辐射强度的数据,以说明可以利用的太阳能的可得性。关于可获得的太阳能的资料将用于估计太阳辐射的强度,以便能最适当地利用太阳能。本研究估算了太阳辐射强度。用来估计太阳辐射强度的数据是空气温度、湿度、太阳辐射持续时间和降雨量。本研究使用的方法是一个反向传播训练的人工神经网络(ANN)。本研究利用1个隐藏层神经元数量的变化,根据RMSE值和相关性得到最佳组。然后用每个训练模拟的最佳组来估计太阳辐射。查亚普拉市的估计结果RMSE值为1,970 kWh/m2。Jayapura地区接收的太阳辐射具有足够高的替代能源潜力,月平均辐射值为4.5 kWh/m2。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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