日太阳总辐射月均值的估算

通过对中国8个典型城市日太阳总辐射实际观测月均值的分析,得到了较为近似的日太阳总辐射月均值模型,并用该模型对郑州地区的数据进行了预测,预测值与实测值吻合良好,表明该模型具有很好的通用性,为中国太阳能的利用提供了科学依据.     

日太阳散射辐射月均值估算模型的对比分析

散射辐射是地表太阳辐射的重要组成部分,其计算十分复杂。文章在前人研究成果的基础上,结合中国亚热带季风气候区的实际情况,以昆明为例,对9种已有的和文章所提出的2种日太阳散射辐射月均值估算模型进行了对比分析,并在武汉和广州对表现良好的模型进行了验证。结果表明,用文章所提出的模型估算昆明、武汉、广州的日太阳散射辐射月均值,具有较其它方法更高的模拟精度。因此,该模型可尝试用于我国亚热带季风气候区日太阳散射辐射月均值的估算。     

基于线性方法的内蒙古地区太阳总辐射月均值估算

探讨了内蒙古地区太阳总辐射月均值与日照百分率的关系,基于5个气象站1996—1998年连续3 a的月日照时数（n）和太阳总辐射值（R_s）。计算得到Angstrom方程的系数a和b,与和清华等拟合得到的中国西部太阳总辐射公式中的a=0.185,b=0.595,比较一致。同时,R_s和n之间的直接线性关系,R与月平均温度（T）之间的直接线性关系也能用来估算太阳总辐射月均值,总均方根误差约为80 MJ·m^-2/month,总百分比误差约为18%。     

新型遮阳系统对太阳散射辐射的遮阳效果研究

通过搭建室外太阳散射辐射得热量的测量装置,对透过遮阳系统进入室内的太阳散射辐射得热量进行了试验研究,并通过建立理论模型对测试结果进行了验证分析,结果表明:该实验装置对透过遮阳系统的太阳散射辐射得热量的测试是可行的,同时通过试验得到了该遮阳系统对太阳散射辐射的遮阳效果。     

基于SVM方法的太阳总辐射值的估算

采用支持向量机（SVM）方法对太阳总辐射值进行了估算,并与传统的线性方法进行了对比分析。对于日太阳总辐射估算,SVM方法的均方根误差为2.27（MJ·m-2）/d,平均百分比误差为22%;而对于月太阳总辐射估算,SVM方法的均方根误差约为21（MJ·m-2）/month,平均百分比误差低于5%。SVM方法提供了一种高精度估算太阳总辐射的新途径。     

估算不同地区月平均太阳散射辐射量数据的研究

采用多项式模型与区间模型 ,对我国不同纬度城市 1 990 - 2 0 0 0年期间的月平均太阳散射辐射量 (Id) ,月平均太阳总辐射量 (I) ,以及晴朗指数 (Kt)之间的关系进行了回归分析。研究结果表明 ,在太阳能与建筑结合工程设计中 ,利用Id I与Kt 的函数关系 ,可以对我国不同地区月平均太阳散射辐射量数据进行初步的估算 ;我国 30°- 40°纬度范围内城市的Kt在 0 .3～ 0 .8之间 ,对于较高或较低纬度范围内部分城市的Kt 可以达到 0 .8- 1 .0 ;Id I-Kt 系曲线可以在一定程度上反映城市的空气质量情况的优劣 ,通常在其它因素相同的条件下 ,城市空气质量越好 ,同归曲线越趋近于线性     

透过玻璃窗的太阳散射辐射得热量测试与模拟研究

通过搭建室外太阳散射辐射得热量的测量装置,对室内太阳散射辐射得热量进行了试验研究,并通过建立理论模型对测试结果进行了验证分析。结果表明:室内太阳散射辐射得热量和室外太阳散射辐射强度随时间规律变化,两者变化趋势一致。多组实验数据验证得出该实验装置对太阳散射辐射得热量的测试是可行的。     

北京地区太阳紫外辐射的基本特征

1990年1月至1991年3月在中国科学院大气物理研究所香河观测站(北京东南70公里处)利用国产TBQ-4型分光辐射表及配套的智能辐射记录仪，对近地面太阳紫外辐射进行了观测，得到此间北京地区近地面太阳紫外辐射及其占太阳总辐射比例的变化特征，并分析了差异的原因。给出了1990年1月至1091年3月太阳紫外辐射的极值及对应的紫外辐射占总辐射的比例，后者同太阳总辐射存在反相关。     

福建太阳总辐射和地面辐射平衡的分布

首先利用福州市的辐射观测资料，建立计算太阳总辐射的经验公式，并计算福建省各市、县的太阳总辐射的年、月平均辐照度。然后利用地区代表站的地面反射率，求得福建省各月、年地面所吸收的太阳辐射能的分布。再利用地面温度、气温、水汽压和总云量、计算得出福建省各地的地面有效辐射的月、年平均辐照度。最后得出福建省各月、年地面辐射平衡的分布。     

Estimation of monthly mean daily diffuse radiation in China

In this study, several equations are employed to estimate monthly mean daily diffuse solar radiation for eight typical meteorological stations in China. Estimated values are compared with measured values in terms of statistical error tests such as mean percentage error (MPE), mean bias error (MBE), root mean square error (RMSE). All the models fit the data adequately and can be used to estimate monthly mean daily diffuse solar radiation from global solar radiation and sunshine hours. This study finds that the quadratic model performed better than the other models:     

Use of radial basis functions for estimating monthly mean daily solar radiation

The present study utilizes the radial basis functions technique for the estimation of monthly mean daily values of solar radiation falling on horizontal surfaces and compares its performance with that of the multilayer perceptrons network and a classical regression model. In this work, we use solar radiation data from 41 stations that are spread over the Kingdom of Saudi Arabia. The solar radiation data from 31 locations are used for training the neural networks and the data from the remaining 10 locations are used for testing the estimated values. However, the testing data were not used in the modeling or training of the networks to give an indication of the performance of the system at unknown locations. Results indicate the viability of the radial basis for this kind of problem.     

Techniques for obtaining the monthly mean hourly diffuse radiation from daily values

The classical equation of Liu and Jordan for estimating the monthly mean hourly to daily diffuse ratio (r_d) was tested for applicability to locations in Spain. While the model predicts the r_d ratios and the monthly mean, hourly diffuse (I_d) values accurately at low hour angles close to solar noon, errors involved in the estimation increase rapidly as one approaches high angles away from solar noon. A technique is suggested to overcome this discrepancy. The proposed method, which employs the monthly mean daylength S_o as the only input parameter, may be used to evaluate the r_d ratios accurately for locations in Spain when measured data are not available. A set of second-order polynomial equations relating r_d and S_o, obtained from the analysis of longterm, measured hourly diffuse radiation data for several stations in Spain, is recommended for estimation purposes.     

Estimating monthly mean valuesof daily total solar radiation for Australia

Monthly mean values of daily total solar radiation were obtained for the widest possible network acrossAustralia. Bureau of Meteorology sources yielded 11 stations with long term records of both measured daily total solar radiation and sunshine hour values. Monthly modified Angstrom equations were developed from these data and used to estimate radiation values for a further 90 stations in the Bureau of Meteorology network that had sunshine hour data. Measured daily total solar radiation data were obtained from a variety of sources mostly outside the Bureau of Meteorology network for an additional 33 stations. Finally, estimates of solar radiation from detailed cloud cover data were used for a further 12 stations, selected because they filled in significant gaps in coverage. These various sources yielded a total of 146 sets of monthly mean values of daily total solar radiation. For each month optimal surfaces, which were functions of position only, were fitted to this network of values using Laplacian smoothing splines with generalized cross validation. Residuals from the fitted surfaces at the data points were acceptably low. Fitted surfaces which included, in addition to position variables, a cloudiness index based on a transform of mean monthly precipitation further reduced these residuals. The latter fitted surfaces permit estimation of monthly mean values of total daily solar radiation at any point on the continent with a root mean square predictive error of no more than 1.25 MJ m⁻² day⁻¹ (5.2 per cent of the network mean) in summer and 0.74 MJ m⁻² day⁻¹ (5.5 per cent of the network mean) in winter.     

Estimation of monthly average daily diffuse radiation for Brunei Darussalam

Solar irradiance data obtained from the Meteorological Department of Civil Aviation, Ministry of Communication, Brunei Darussalam for a period of 10 years was analysed to study the diffuse radiation over four districts, namely Brunei/Muara, Belait, Tutuong and Temburong. It was observed that including more than one measured variable improves the accuracy of the estimation of diffuse radiation.     

Statistical comparison of models for estimating the monthly average daily diffuse radiation at a subtropical African site

Nine correlations have been developed in this paper to estimate the monthly average diffuse radiation for Dakar, Senegal. A 16-year period data on the global (H) and diffuse (H_d) radiation, together with data on the bright sunshine hours (N), the fraction of the sky’s cloud cover (Ne/8), the water vapour pressure in the air (e) and the ambient temperature (T) have been used for that purpose. A model inter-comparison based on the MBE, RMSE and t statistical tests has shown that estimates in any of the obtained correlations are not significantly different from their measured counterparts, thus all the nine models are recommended for the aforesaid location. Three of them should be particularly selected for their simplicity, universal applicability and high accuracy. Those are simple linear correlations between and , or . Even presenting adequate performance, the remaining correlations are either simple but less accurate, or multiple or nonlinear regressions needing one or two input variables.     

Effect of sunshine and solar declination on the computation of monthly mean daily diffuse solar radiation

Several years of measured data for 17 European locations have been used to develop models for estimating monthly mean daily values of diffuse radiation (Hd) from combinations of the following: clearness index, sunshine fraction, and solar declination. Two models giving the highest correlation coefficients and the lowest standard errors of estimation are tested with data for 10 European locations not used in their development. From consideration of the MBE and RMSE values, a model which estimates Hd values from clearness index, relative sunshine duration and solar declination is found to be the most accurate. Comparison with Hd values predicted with the European Community solar radiation model (ECM) confirms this conclusion.