Global solar radiation (GSR) is a critical variable for designing photovoltaic cells, solar furnaces, solar collectors, and other passive solar applications. In Nepal, the high initial cost and subsequent maintenance cost required for the instrument to measure GSR have restricted its applicability all over the country. The current study compares six different temperature-based empirical models, artificial neural network (ANN), and other five different machine learning (ML) models for estimating daily GSR utilizing readily available meteorological data at Biratnagar Airport. Amongst the temperature-based models, the model developed by Fan et al. performs better than the rest with an of 0.7498 and RMSE of . Feed-forward multilayer perceptron (MLP) is utilized to model daily GSR utilizing extraterrestrial solar radiation, sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity as inputs. ANN3 performs better than other ANN models with an of 0.8446 and RMSE of . Likewise, stepwise linear regression performs better than other ML models with an of 0.8870 and RMSE of . Thus, the model developed by Fan et al. is recommended to estimate daily GSR in the region where only ambient temperature data are available. Similarly, a more robust ANN3 and stepwise linear regression models are recommended to estimate daily GSR in the region where data about sunshine duration, maximum and minimum ambient temperature, precipitation, and relative humidity are available.

中文翻译：

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基于温度的经验模型和机器学习技术的评估，以估计尼泊尔比拉特纳加尔机场的每日全球太阳辐射

全球太阳辐射（GSR）是设计光伏电池，太阳能炉，太阳能收集器和其他被动太阳能应用的关键变量。在尼泊尔，用于测量GSR的仪器所需的高昂的初始成本和后续维护成本限制了其在全国的适用性。当前的研究比较了六个不同的基于温度的经验模型，人工神经网络（ANN）和其他五个不同的机器学习（ML）模型，这些模型用于利用比拉特纳加尔机场随时可用的气象数据来估算每日GSR。在基于温度的模型中，Fan等人开发的模型。的效果优于其余的0.7498和的RMSE 。前馈多层感知器（MLP）用于利用地外太阳辐射，日照时长，最高和最低环境温度，降水和相对湿度作为输入来模拟每日GSR。ANN3的性能优于其他ANN模型，其值为0.8446，RMSE为。同样，逐步线性回归的性能优于其他ML模型，后者的值为0.8870，RMSE的值为。因此，范等人开发的模型。建议仅在只有环境温度数据的区域内估算每日GSR。同样，建议使用更健壮的ANN3和逐步线性回归模型来估计可获得日照时间，最高和最低环境温度，降水和相对湿度的数据的地区的每日GSR。