天气类型对逐时太阳总辐射模型适应性的影响

将分解模型（decomposition model）按照输入参数、构造方法及特性分为3类,选择拉萨、西安、上海2016年辐射观测值作为实验数据,引入精确度分值（accuracy score, AS）作为模型适应性评判标准,研究晴、多云、阴3种天气类型下分解模型适应性规律以及针对不同天气类型适应模型形式。结果表明天气类型对模型的适应性有显著影响,即由晴到阴模型整体适应性呈下降趋势;当天气特别晴朗时第Ⅱ类模型适应性明显优于其他两类模型,而对于其他天气类型第Ⅰ类的C.P.R模型适应性最好。利用天气类型适应模型,3个地区全年逐时总辐射误差平均可减小3%,平均绝对误差百分率（PMAE）在20%~25%之间,均方根误差百分率（PRMSE）在26%~32%之间。可见利用天气适应模型能够有效减小估算误差,逐时总辐射数据精度能够满足建筑节能设计需求。     

太阳逐时总辐射计算模型适用性分析——以我国部分地区为例

采用拉萨、狮泉河、那曲、昌都地区1994~2004年的日总辐射和逐时总辐射的实测数据,选取8种典型的统计模型进行逐时总辐射量的计算和验证,分析比较实测数据和计算数据在全年及不同天气状态下的相关系数、误差指标,得出Collares-Pereira and Rabe模型计算逐时辐射量的准确率最高,并且该类模型在晴空指数较高时适用性较好。     

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。     

基于气象要素修正的逐时总辐射模型研究——以西安地区为例

该文引入逐时气象要素对分解模型进行修正,针对C.P.R模型,提出一种基于日照时数、温度以及相对湿度的多气象要素修正模型。基于西安2015年10月1日—2017年7月31日观测数据,分析3种天气类型下模型修正前、后的计算误差,结果显示多气象要素修正模型的各项误差指标均低于未修正模型,且对于多云天气的修正效果最明显,平均可达2%。基于多气象要素的C.P.R修正模型考虑了气象及辐射观测数据现状,可为其他地区计算水平面逐时总辐射提供方法借鉴。     

基于小波网络的次日太阳逐时总辐射预测技术研究

现有的地面太阳逐时总辐射预测模型的预测精度及泛化能力尚不能令人满意。利用小波神经网络在提升非线性函数影射能力方面的优势,以及递归网络的优良的动态性能,建立了对角递归小波BP网络（DRWBPN）模型,用以对次日地面太阳逐时总辐射进行精确预测。进一步提高预测精度的措施还包括将ASHRAE太阳辐射确定性模型的计算结果和经模糊化处理的气象预报中的云量信息加入到网络输入向量中,以充分利用已知可靠信息。采用分阶段训练网络的方法,提高了有限次数下的训练质量。太阳逐时总辐射预测实例及与其它典型模型预测结果的比较表明,提出的地面太阳逐时总辐射预测模型具有更高精度和实际可行性。     

典型天气下太阳直接辐照的环日比参量时变特性研究

太阳直射的环日比参量（CSR）反映了直接辐射中的能量分布。CSR为时变量,受气溶胶浓度、成份和阳光穿越大气厚度影响。然而,在太阳热发电的聚光能流计算中,通常将其等效为常数而忽略其时变性,造成误差。文章搭建了CSR测量平台,利用双视野辐照表法对北京地区典型天气的CSR时变特性进行了连续测量。结果显示,晴朗天气下,CSR一天内呈现明显的“浴盆”曲线,太阳高度角较小时,CSR可达0.2～0.4;正午时,太阳高度角最大,CSR可降至0.05～0.2,日内变化数倍,多云天气下短时间内环日比呈现出强烈波动。这说明在太阳热发电站的聚焦能流研究中应当将CSR作为时变量对待。文章进一步揭示出CSR具有季节规律,且与大气质量和太阳直接辐照之间存在一定相关性。     

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%。     

日太阳散射辐射月均值的估算

建筑物空调系统的全年或季节能耗分析和太阳能系统的设计分析,需要已知太阳总辐射和散射辐射数据。我国大多数地区只有总辐射观测,没有直接辐射或散射辐射观测,因此需要有一种散射辐射的估算方法。根据全国8个典型城市的实测数据．分析了日散射辐射和日总辐射与日照时长的关系,分别建立了日散射月均值的多项式拟合模型。经过郑州地区数据的检验认为,模型有很好的拟合性能,该模型可以作为通用模型。     

极端天气对被动式太阳能建筑采暖效率的影响

为研究极端天气对被动式太阳能建筑采暖效率的影响,选取关键气象参数（直射辐射、干球温度）来构造极端冷气象条件,主要分析太阳低辐射和极端低温对拉萨、乌鲁木齐、兰州、哈尔滨4个城市的被动式太阳能建筑采暖效率的影响。结果表明：随着太阳直射辐射和温度的降低,哈尔滨整个采暖季的采暖潜力下降最大,拉萨、兰州、乌鲁木齐在太阳直射辐射下降超过50%时,采暖潜力下降最大,哈尔滨在辐射下降43%时采暖潜力下降最大。在温度下降不超过10℃的情况下,拉萨和哈尔滨的采暖潜力下降最大,兰州和乌鲁木齐则是在温度分别下降11和15℃时,采暖潜力下降最大。气象参数对采暖潜力的敏感性分析表明,太阳直射辐射对太阳能采暖潜力的影响大于温度。同时,对不同设计因素进行全局敏感性分析,建筑保温材料的物理参数对采暖潜力的影响高于其他设计参数。     

神经网络模型在逐时太阳辐射预测中应用

设计了一种基于遗传算法的神经网络太阳辐射预测模型。该模型结合了历史逐时辐射数据和气象要素数据,并在训练和预测时加入了温度日较差和天气类型预报参数。还设计了预测因子选择方法、输入资料的处理方法和结果误差评估方法。利用武汉站2007年至2008年8月辐射数据对模型进行了训练,并对2009年8月的逐时辐射进行了诊断预报。预测结果表明,预测模型在天气类型稳定的情况下具有较高的精度,能够反映太阳辐射的日变化状况和辐射量级大小,但在天气类型剧烈变化的情况下预测精度有限。     

中国不同气候区日总太阳辐射计算模型适用性分析及通用计算模型优化

利用中国5个气候区96个气象台站的1981—2010年的日值气象数据,对比分析12个基于日照百分率和12个基于温度的日总太阳辐射计算模型在不同气候区的适用性。采用判定系数（R²）、均方根误差（RMSE）、平均绝对误差（MABE）、平均误差（MBE）和全局性能系数（GPI）5个评价指标,确定各气候区最适宜的模型形式。以该模型为基准,建立适用于中国不同气候区的基于日照百分率和基于温度的日总太阳辐射通用计算模型。结果表明,三次方形式的基于日照百分率和基于日较差-平均温度的模型在各气候区计算精度均最高;以该模型为基础,建立适用于中国不同气候区的基于日照百分率和基于温度的日总太阳辐射通用计算模型,其平均R²分别为0.91和0.68。     

中国不同气候区逐日直散分离模型适用性分析及通用计算模型优化

利用中国5个气候区59个气象台站1981——2010年的日值气象数据,对比分析8个散总比和3个散射系数直散分离模型在中国不同气候区的适用性。采用判定系数（R2）、均方根误差（RMSE）、平均绝对误差（MABE）、平均误差（MBE）和全局性能系数（GPI）5个误差评价指标,确定各气候区最适宜的模型形式。以该模型为基础,建立适用于中国不同气候区的散总比和散射系数逐日直散分离通用模型。结果表明,除线性形式散射系数模型精度较差外,其他模型计算精度均较高,散总比和散射系数模型平均R2分别为0.85和0.62;基于晴空指数-日照百分率的二次多项式散总比模型和基于日照百分率三次多项式散射系数模型在不同气候区精度均最高;以该模型为基础建立中国不同气候区散总比和散射系数逐日直散分离通用模型,其平均R2分别为0.89和0.70。     

A revised empirical model to estimate solar radiation using automated surface weather observations

The transition from manual to automated weather observations at US National Weather Service Offices has compromised the ability to use these data as a means for estimating global horizontal and direct solar radiation. The creation of long term model-derived solar radiation climatologies continues to rely on the in situ cloud data that these observations provide, since homogeneous and readily available satellite data does not span the transition. An existing semi-physical solar radiation model is revised to allow for the estimation of hourly solar radiation based on these observations. Model evaluation reveals that errors in solar radiation estimates are comparable to other contemporary solar radiation models that estimate global horizontal solar radiation on both daily (10–15% mean absolute error) and hourly (15–19% mean absolute error) timescales. Hourly mean absolute errors are similar for different sky conditions, while daily percent errors are similar between seasons. Model updates also allow for accurate estimates of solar radiation in various climate regimes; regional patterns in model bias are not evident.     

A simplified model for generating sequences of global solar radiation data for isolated sites: Using artificial neural network and a library of Markov transition matrices approach

The purpose of this work is to develop a hybrid model which will be used to predict the daily global solar radiation data by combining between an artificial neural network (ANN) and a library of Markov transition matrices (MTM) approach. Developed model can generate a sequence of global solar radiation data using a minimum of input data (latitude, longitude and altitude), especially in isolated sites. A data base of daily global solar radiation data has been collected from 60 meteorological stations in Algeria during 1991–2000. Also a typical meteorological year (TMY) has been built from this database. Firstly, a neural network block has been trained based on 60 known monthly solar radiation data from the TMY. In this way, the network was trained to accept and even handle a number of unusual cases. The neural network can generate the monthly solar radiation data. Secondly, these data have been divided by corresponding extraterrestrial value in order to obtain the monthly clearness index values. Based on these monthly clearness indexes and using a library of MTM block we can generate the sequences of daily clearness indexes. Known data were subsequently used to investigate the accuracy of the prediction. Furthermore, the unknown validation data set produced very accurate prediction; with an RMSE error not exceeding 8% between the measured and predicted data. A correlation coefficient ranging from 90% and 92% have been obtained; also this model has been compared to the traditional models AR, ARMA, Markov chain, MTM and measured data. Results obtained indicate that the proposed model can successfully be used for the estimation of the daily solar radiation data for any locations in Algeria by using as input the altitude, the longitude, and the latitude. Also, the model can be generalized for any location in the world. An application of sizing PV systems in isolated sites has been applied in order to confirm the validity of this model.     

Assessment of diffuse solar energy under general sky condition using artificial neural network

In this paper, artificial neural network (ANN) models are developed for estimating monthly mean hourly and daily diffuse solar radiation. Solar radiation data from 10 Indian stations, having different climatic conditions, all over India have been used for training and testing the ANN model. The coefficient of determination (R²) for all the stations are higher than 0.85, indicating strong correlation between diffuse solar radiation and selected input parameters. The feedforward back-propagation algorithm is used in this analysis. Results of ANN models have been compared with the measured data on the basis of percentage root-mean-square error (RMSE) and mean bias error (MBE). It is found that maximum value of RMSE in ANN model is 8.8% (Vishakhapatnam, September) in the prediction of hourly diffuse solar radiation. However, for other stations same error is less than 5.1%. The computation of monthly mean daily diffuse solar radiation is also carried out and the results so obtained have been compared with those of other empirical models. The ANN model shows the maximum RMSE of 4.5% for daily diffuse radiation, while for other empirical models the same error is 37.4%. This shows that ANN model is more accurate and versatile as compared to other models to predict hourly and daily diffuse solar radiation.     

气象观测站点外地区典型气象年数据的获取方法

为了解决基础气象要素的缺失造成的建筑设计数据不足的被动状况以及无法准确科学提供室外基础数据的问题,以国家基准、基本和一般气象站的原始记录数据为基础,使用逆距离加权插值法（IDW）和梯度逆距离加权插值法（GIDW）,针对实验结果进行误差分析,研发西北和东南地区气象观测站点以外TMY数据的获取方法,并利用数学模型生成待测点的逐时数据。结果表明,使用GIDW方法得到的气象数据与实际值的变化趋势相似;同时,在考虑海拔影响后,使用GIDW方法比IDW方法的插值结果误差更小;对逐时数据进行处理,待测点的生成数据与实际逐时值符合度好。     

Estimation of global solar radiation over the city of La Serena (Chile) using a neural network

An artificial neural network for the estimation of hourly global solar radiation in La Serena (Chile), was developed using data measured from a meteorological station. La Serena city (29°54′ S, 71°15′ W) is located in the bay area at south of the hyper-arid Atacama Desert. In this study, 25123 data points of global solar radiation of 5 years (2001–2005) were used to train the network and then 7618 data points of global solar radiation not used in the training process were predicted (years 2006 and 2007). The meteorological data used in the model were: wind speed, relative humidity, air temperature, and soil temperature. The results were compared with the real data and other models available in the literature, and shows that the neural network obtained can be properly trained and can estimate the hourly global radiation with acceptable accuracy.     

辐射对太阳电池光电输出性能影响的定量研究

该文定量研究电子和质子辐射对太阳电池输出特性的影响。首先,证实作者前期工作得到的太阳电池输出电流-电压（I-V）模型仍适用于高能粒子辐射后的太阳电池;其次,由太阳电池输出电流-电压特征量定义一个等效电阻（Req）。采用最小二乘方曲线拟合方法,找到能够定量描述太阳电池能量转化效率（PCE）与等效电阻（Req）的关系,并且定量解释了经历电子和质子辐照的太阳电池的等效电阻（Req）同辐照剂量的关系。最后,扩展这个模型用于定量描述太阳电池外量子效率（EQE）与入射光子能量（hν）的关系,经拟合验证,该模型与实验数据十分吻合,理论同实验结果的相关系数R大于0.98,平均相对误差（ARE）小于3%。     

Study of hourly and daily solar irradiation forecast using diagonal recurrent wavelet neural networks

An accurate forecast of solar irradiation is required for various solar energy applications and environmental impact analyses in recent years. Comparatively, various irradiation forecast models based on artificial neural networks (ANN) perform much better in accuracy than many conventional prediction models. However, the forecast precision of most existing ANN based forecast models has not been satisfactory to researchers and engineers so far, and the generalization capability of these networks needs further improving. Combining the prominent dynamic properties of a recurrent neural network (RNN) with the enhanced ability of a wavelet neural network (WNN) in mapping nonlinear functions, a diagonal recurrent wavelet neural network (DRWNN) is newly established in this paper to perform fine forecasting of hourly and daily global solar irradiance. Some additional steps, e.g. applying historical information of cloud cover to sample data sets and the cloud cover from the weather forecast to network input, are adopted to help enhance the forecast precision. Besides, a specially scheduled two phase training algorithm is adopted. As examples, both hourly and daily irradiance forecasts are completed using sample data sets in Shanghai and Macau, and comparisons between irradiation models show that the DRWNN models are definitely more accurate.