太阳能光伏发电预报网站系统设计与实现

基于中尺度数值预报模式,以原理预报法、动力—统计预报法等太阳能光伏发电量预报方法为理论基础,构建了太阳能光伏发电预报系统,并根据太阳能发电预报的产品显示需求,设计了太阳能光伏发电预报网站的总体功能,基于ASP.Net 4.0和Silverlight 4.0技术开发了太阳能光伏发电预报网.预报结果在预报员确认后经网站采用不需要终端用户部署的B/S模式展示和分发.     

Fuzzy model representation of thermosyphon solar water heating system

The aim of this paper is to focus on improvement in prediction accuracy of model for thermosyphon solar water heating (SWH) system. The work employs grey-box modeling approach based on fuzzy system to predict the outlet water temperature of the said system. The prediction performance results are compared with neural network technique, which has been suggested by various researchers in the last one decade. The outlet water temperature prediction by fuzzy modeling technique is analyzed by using 3 models, one with three inputs (inlet water temperature, ambient temperature, solar irradiance), next with two inputs (inlet water temperature, solar irradiance) and last one with single input (solar irradiance/inlet water temperature). An improved prediction performance is observed with three inputs fuzzy model.     

晴空工况光伏超短期功率预报方法

提出一种基于实时辐射采集技术的晴空工况光伏超短期功率预报方法.该方法预报时效为未来0～4h,预测点时间分辨率为15 min,每15 min滚动循环预测.以此预报方法为基础,建立了国家能源太阳能发电研发(实验)中心屋顶光伏电站的功率预测系统.现场预测结果表明,该方法具有较好的预报效果,能够满足工程应用要求.     

Measurements based dynamic climate observer

This paper introduces a dynamic prediction of solar radiation and ambient temperature. Medium term prediction is based on climatic parameters behaviours during the day before and on time distribution models. As for short term prediction, it is ensured by an ARMA predictor using Kalman filter. Herein, we describe our method and present prediction results. Validation is based on measures taken during the year 2005 in the north of Tunisia. The work effectiveness is illustrated by a short term and medium term prediction of the electric energy produced by a 1 KWp photovoltaic panel (PVP) installed at the Energy and Thermal Research Centre (CRTEn) in the north of Tunisia. Since our work delivers accurate climatic parameters prediction, the obtained results can be easily adapted to predict any other solar conversion system output.     

基于系统辨识的地表太阳辐射Box-Jenkins模型的建立

利用天文辐射作为输入数据,采用系统辨识的方法得到地表太阳辐射的BJ(Box-Jenkins)模型,并通过残差分析和零极点检验。该方法可用于预测5～15min时间间隔的地表太阳辐射,为太阳能电站的功率输出预测提供太阳能辐射数据。     

Reliability analysis of solar photovoltaic system using hourly mean solar radiation data

This paper presents the hourly mean solar radiation and standard deviation as inputs to simulate the solar radiation over a year. Monte Carlo simulation (MCS) technique is applied and MATLAB program is developed for reliability analysis of small isolated power system using solar photovoltaic (SPV). This paper is distributed in two parts. Firstly various solar radiation prediction methods along with hourly mean solar radiation (HMSR) method are compared. The comparison is carried on the basis of predicted electrical power generation with actual power generated by SPV system. Estimation of solar photovoltaic power using HMSR method is close to the actual power generated by SPV system. The deviation in monsoon months is due to the cloud cover. In later part of the paper various reliability indices are obtained by HMSR method using MCS technique. Load model used is IEEE-RTS. Reliability indices, additional load hours (ALH) and additional power (AP) reduces exponentially with increase in load indicates that a SPV source will offset maximum fuel when all of its generated energy is utilized. Fuel saving calculation is also investigated. Case studies are presented for Sagardeep Island in West Bengal state of India.     

A new method for typical weather data selection to evaluate long-term performance of solar energy systems

A new method is proposed for determining typical 1-year weather data from a multi-year record for evaluation of solar energy systems. The procedure is very straightforward and can be utilised with ease when determining the long-term performance of a solar hot water system (SHWS). It is made up of a concatenation of 12 months individually selected from a multi-year database. The criterion for the selection is the minimisation of error in the monthly solar gain prediction of the system. Considering this criterion, the ‘typicality’ of the weather pattern is taken into account, in addition to its influence on the behaviour of the solar system. A comparison is made between the new method and others frequently referred to in the literature. Based on simulation results for yearly, monthly and daily power delivered, six indicators have been calculated. These indicators quantify the different behaviours of the system when ‘historical’ and typical weather data are applied. The all inclusive comparison shows that the new method for deriving typical weather data leads to an accurate evaluation of the long-term performance of a SHWS.     

A grey‐box model of next‐day building thermal load prediction for energy‐efficient control

Accurate building thermal load prediction is essential to many building energy control strategies. To get reliable prediction of the hourly building load of the next day, air temperature/relative humidity and solar radiation prediction modules are integrated with a grey‐box model. The regressive solar radiation module predicts the solar radiation using the forecasted cloud amount, sky condition and extreme temperatures from on‐line weather stations, while the forecasted sky condition is used to correct the cloud amount forecast. The temperature/relative humidity prediction module uses a dynamic grey model (GM), which is specialized in the grey system with incomplete information. Both weather prediction modules are integrated into a building thermal load model for the on‐line prediction of the building thermal load in the next day. The validation of both weather prediction modules and the on‐line building thermal load prediction model are presented. Copyright © 2008 John Wiley & Sons, Ltd.     

Load optimization in solar space heating systems

When load variables, such as window and insulation types, are included in the economic optimization of a solar space heating system, the over-all cost is lower than that resulting from optimization of collection area for a fixed load (as by FCHART [1] and SOLCOST [2]). In this paper an algorithm is derived for choosing insulation levels, as well as solar collection area, so as to minimize the over-all cost of constructing and heating a building. The general algorithm is applicable with any solar performance prediction method, and with any economic criterion where the “cost” is a linear function of collection area and of auxiliary energy consumption. A specific algorithm is also derived for active solar systems using the Relative Areas method of performance prediction [3] and a conventional present worth life cycle cost analysis. The degree-day model is used for the load calculations.     

卫星太阳翼在轨输出功率预测模型

该文介绍一种卫星太阳翼在轨功率输出预测方法,由单体太阳电池根据固体物理理论推导出来直流理论分析模型获得其等效电路,通过对单体太阳电池串、并联后组成的太阳翼电气电路,获得太阳翼等效电路,并根据该等效电路推导出太阳翼的直流分析模型。将太阳翼的直流分析模型转化为由单片太阳电池片开路电压Voc、短路电流Isc、最大功率点电压Vmp、最大功率点电流Imp这4个参数决定的太阳翼工程应用方程。同时,通过地面试验获得单体太阳电池的电压和电流衰降系数,获取太阳翼实际在轨不同时刻的开路电压VAoc、短路电流IAsc、最大功率点电压VAmp、最大功率点电流IAmp,并通过计算获取太阳翼工作点电压、电流,得到太阳翼的在轨预测工作输出功率。通过将该文模型预测值与太阳翼实际在轨输出电流、电压遥测值进行比较,验证该预测模型的有效性。该预测模型可通过单体太阳电池的4个工程参数,获得整个太阳翼的直流分析模型,便于太阳翼设计阶段建模分析的工程化应用。     

基于多维特征分析的双层协同太阳辐照度预测

为增强逐日太阳辐照度预测的准确性和普适性,提出一种基于多维特征分析的双层协同预测模型。首先,搭建一种双层协同架构,将整个模型分成基准层和提升层两部分,使用分层预测的方式追踪目标对象的多维特征和变化趋势;其次,以数值天气预报（NWP）为输入,采用LightGBM基于特征学习预测方法构建基准预测模型;然后,在前者的基础上,挖掘目标时刻太阳辐照度与历史时序数据之间的关联性,引入改进AdaBoost算法与多隐层极限学习机（MH-ELM）作为提升层主体,提高时序预测的稳定性;最后,选用中国中部地区某光伏电站实测太阳辐照度数据进行算例分析,验证了该模型的合理性和有效性。     

Online multi-step prediction for wind speeds and solar irradiation: Evaluation of prediction errors

We propose a general method for predicting multiple steps ahead of our target system and estimating simultaneously the prediction errors in a real time. The requirement of the proposed method is that we have a time series of the target system. We demonstrate the method by artificial data, real wind speed data, and real solar irradiation data.     

基于BP神经网络的印染太阳能集热温度预测

温度在印染加工最重要的工艺参数,因此对太阳能的集热能力做出及时预测是太阳能集热系统在印染行业成功应用的关键,但太阳能集热受到纵多因素的影响,单纯的数学模型难以准确预测。文中采用神经网络对太阳能集热系统进行集热温度预测,根据太阳辐照度、实测温度、天气情况、进水温度等环境参数,预测印染太阳能集热系统的集热温度。结果表明,该方法速度快,结果较准确,为印染太阳能集热控制系统的设计提供了一个新的预测手段。     

Successive lumped analysis simulation of solar energy collection systems

A methodology for deriving an analytical, simplified solar system performance prediction model is presented. Factors related to system design can be examined; for example, storage tank temperature variations, solar system turn-on and turn-off times and system ‘memory’. A simple domestic water heating type system is used as a basis for illustrating derivation of performance models. Predicted performance results are in good agreement with results obtained from another design method. A generalized procedure is described for deriving new system models. The lumped analysis simulation may prove useful for non-standard applications where other simplified design methods are not defined and where detailed simulation capability is not available.     

An analytic model for the long-term performance of solar air heating systems

An analytic model is presented for the prediction of the monthly and yearly thermal performance of solar air heating systems. The effects of stratification in packed rock bed storage are explicitly taken into account. An expression for monthly solar fraction that depends on the important system and climatic variables is derived, which affords accurate predictions compared to corresponding f-chart calculations. The value of our analytic model, vis-a-vis calculational tools such as f-chart, is discussed. The method is applicable to all solar collector types as well as to load distributions of other than 24 hr/day.     

Design nomogram for long-term performance prediction of Integrated Collector–Storage (ICS) solar water heater

The performance prediction of Solar Integrated Collector–Storage System (ICS) is determined in terms of generalized dimensionless grouped parameters. These dimensionless parameters are primarily a combination of physical characteristics representing thermal performance curves for solar ICS systems and include information readily available to a designer pertaining to dimensions, thermal characteristics and operating conditions. The inter-relation between these is summarised as a nomogram and helps in predicting the system performance graphically for a particular locality or it may be deduced from it by simple hand calculation. This method does not require a detailed knowledge of system parameters for predicting system performance, and an advantage of this method is that short-term performance data is used to predict long-term performance and solar fraction. A simulation model was developed using a transient one-dimensional analysis for a solar ICS system. Time-dependent heat transfer coefficients and thermophysical properties were taken in the present simulation. © 1998 John Wiley & Sons, Ltd.     

Analysis of generated power of ETS-VII during solar activity maximum period

The generated power degradation of a satellite in a low earth orbit during high solar activity period has been compared with the power degradation of a satellite during low solar activity period. A degradation prediction method is developed for this study. As a result, the effect of a large solar flare on solar cell degradation is found to be negligible in a low earth orbit. This is because the effects of shield thickness and inclination are thought to be greater than that of degrees of solar activity.     

A design method for parallel solar-heat pump systems

In this paper, a method is developed for predicting the performance of parallel solar-heat pump systems. This procedure requires as inputs the fraction of the space and water heating load met by solar energy, and the fraction of the load that would have been met by the same heat pump operating without a solar system (a stand-alone system). The procedure then combines these results in a way which accounts for the interaction of the solar system and the heat pump and yields the performance of the combined system. The purchased energy fractions determined from this procedure are compared to those from detailed simulations. The standard deviation of the prediction errors are within 1.3 per cent of the load, and within the accuracy with which system parameters are known.     

Calculation of monthly average insolation on a shaded surface at any tilt and azimuth

Estimation of the monthly average solar radiation on surfaces of arbitrary orientation is necessary for many solar performance prediction programs and is useful for other applications. For passive solar applications, especially, overhangs are often used to seasonally modulate the amount of radiation striking the surface.

Liu and Jordan[1] have developed a method for estimation of monthly average radiation on unshaded tilted surfaces based on horizontal surface data. This method has been extended to unshaded surfaces of arbitrary tilt and azimuth by Klein[2]. Utzinger and Klein[3] have presented a graphical method for estimating monthly average radiation on shaded vertical surfaces, while Jones [4]has offered an analytical method for the same configuration. This paper presents an analytical solution to the calculation of monthly average insolation on shaded surfaces at any tilt and azimuth. Results are comparable among the three methods when shaded vertical surfaces are analyzed.

This analytical method offers an alternative to slower and less accurate numerical integration and to less general regression of numerical integration results for use in solar performance prediction programs.     

Prediction of the performance of solar heating systems over a range of storage capacities

A new method for prediction of the performance of solar heating systems using well-mixed storage is presented which predicts monthly and annual system performance (relative to a computer simulation) over a wide range of system variables including minimum or base storage temperature, storage capacity, and geographic location. The method relies on heavily pre-processed site-specific radiation and weather data which is used with system properties to predict the quantities necessary for correlation. The method yields long-term monthly and annual performance predictions which are so accurate that they can serve simultaneously for preliminary design, economic optimization, and final design, eliminating the need for simulation.