Comparison of methodologies for tmy generation using 20 years data for Athens, Greece

The need of accurate Test Reference Years (TRYs) for simulations has been well recognised over the years. Various methods for deriving TRYs have been developed, but their final results can be significantly different. In this paper, the major methodologies reported in literature were applied to 20-year hourly measurements of weather data from Athens, covering the period 1977 to 1996. Seventeen TRYs were produced in total. The basis to select the “best” performing TRY includes meteorological criteria (inherent in the selection process used by each method) and comparisons of results from various simulations for typical energy systems (i.e. a solar water heater, a building, a large scale solar heating system with interseasonal storage and a photovoltaic system). Based on the results of each simulation exercise, a scoring system was developed and applied. The best performing TRY was found to be the one produced by a modified Festa-Ratto method.     

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.     

A model for calculating hourly global solar radiation from satellite data in the tropics

A model for calculating global solar radiation from geostationary satellite data is presented. The model is designed to calculate the monthly average hourly global radiation in the tropics with high aerosol load. This model represents a physical relation between the earth-atmospheric albedo derived from GMS5 satellite data and the absorption and scattering coefficients of various atmospheric constituents. The absorption of solar radiation by water vapour which is important for the tropics, was calculated from ambient temperature and relative humidity. The relationship between the visibility and solar radiation depletion due to aerosols was developed for a high aerosol load environment. This relationship was used to calculate solar radiation depletion by aerosols in the model. The total column ozone from TOMS/EP satellite was employed for the determination of solar radiation absorbed by ozone. Solar radiation from four pyranometer stations was used to formulate the relationship between the satellite band earth-atmospheric albedo and broadband earth-atmospheric albedo required by the model. To test its performance, the model was used to compute the monthly average hourly global radiation at 25 solar radiation monitoring stations in tropical areas in Thailand. It was found that the values of monthly average of hourly global radiations calculated from the model were in good agreement with those obtained from the measurements, with the root mean square difference of 10%. After the validation the model was employed to generate hourly solar radiation maps of Thailand. These maps reveal the diurnal and season variation of solar radiation over the country.     

Iterative filtering of ground data for qualifying statistical models for solar irradiance estimation from satellite data

A new technique of filtering solar radiation ground data is proposed for generating models for solar irradiance estimation from geostationary satellite data. The filtering processes consists of an iterative way of selecting the training data set to achieve the best model response. Although in this paper the proposed methodology has been used for solar irradiance modeling, it could be applied to any kind of empirical modeling. The iterative filtering method has proven to have fast convergence and to improve successfully the statistical model response, when applied to hourly global irradiance calculation from satellite-derived irradiances for 13 Spanish locations. Individual statistical models for hourly global irradiance were fitted using the Heliosat I method applied to Meteosat images of 13 Spanish stations for the period 1994–1996.     

Global solar radiation in Northeastern Saudi Arabia

This paper presents the actual global solar radiation on a horizontal surface along with the prevailing meteorological conditions encountered during the measurement period from 1 January–31 December, for one complete year, in the Arabian Gulf Coast near the city of Dhahran. High resolution, real time solar radiation and meteorological data were collected, and processed. Hourly, daily, and monthly statistics of solar radiation was made from the one-minute averaged recorded values. The highest measured daily, and monthly mean solar radiation were found to be 351 and 328 W/m⁻², respectively. The highest one-minute averaged solar radiation values up to 1183 W/m⁻² were observed in the summer season, from May–September. The highest hourly solar radiation value was recorded as 1053 W/m⁻² in the middle of June. Beside the global solar radiation measurements, the main observed meteorological parameters were temperature, pressure, wind speed, precipitation, and relative humidity. On the other hand, the estimation of daily and monthly mean global solar radiation was performed based upon two empirical formulas which relate the solar radiation to the sunshine duration, relative humidity, maximum temperature, the latitude of the monitoring location, sunset hour and declination angles. The agreement between the measured and estimated solar radiation values was found to be satisfactory. Nevertheless, the empirical formula under-estimates the solar radiation values during summer, and over-estimates during winter.     

Generation of operational maps of global solar irradiation on horizontal plan and of direct normal irradiation from Meteosat imagery by using SOLARMET

The physical model SOLARMET, elaborated in ENEA (Italian National Agency for New Technologies, Energy and Environment), provides hourly average global solar irradiance on a horizontal surface (GHi) and hourly average direct normal solar irradiance (DNi) for Italy based on primary satellite images in the visible band.

In the present study, the hourly estimates of surface radiation generated by SOLARMET have been summed up to produce monthly average daily irradiation maps. Hourly and monthly maps were done for the years 1996 and 2002. The parameters of this model were obtained by comparing the Meteosat satellite data with ground data gathered in 2002. Differences, relative to 1996, between SOLARMET estimates and observations obtained over two radiation networks of Italian ground sites are presented: the Meteorological Service of the Italian Air Force and National Agro-Meteorological Network; In total 51 ground stations. The comparison between SOLARMET and the previous Italian method carried out in ENEA shows an improvement due to SOLARMET. Such comparison between the values derived using SOLARMET and previous ENEA methodologies and with data from ground-based stations was possible only for monthly averages of daily global radiation due to an almost total lack of direct radiation ground data in Italy.

The operational monthly solar radiation maps, showing solar energy potentials, permit the selection of construction sites to solar energy project developers. In Italy, these data are necessary for installing solar thermal concentration power plants in support of the R&S program recently funded to demonstrate the possibility of these technologies.     

西沙地区建筑能耗模拟用典型气象年研究

典型气象年可为建筑节能设计提供必要的气象数据条件.在挑选典型气象年的过程中,通常要具备30 a的气象数据资料,包括如干球温度、太阳辐射、风速、露点温度等.中国南海岛礁地区地面气象观测数据缺失问题严重,特别是太阳辐射数据较难获得.针对这一问题,以西沙地区为例,基于中国气象局提供的近30 a(1985—2014)实测气象数...     

Estimation of the energy output of a photovoltaic power plant in the Austrian Alps

An output simulation is one of the first steps in planning a photovoltaic power plant (PVP) at a certain location. Various computer codes already exist to assess the energy output of a PVP, when fed with the relevant meteorological data. For the alpine area, however, there is no appropriate data considering the altitude of the site available. A computer code has been written to check the available raw data for plausibility and to fill in missing data synthetically. On the basis of 6 yr of measured data a test reference year (TRY) has been developed for the town of Leonding and the summit Loser. They are located just 80 km apart but have a 1250 m difference in their altitudes. For comparison, these new TRYs, together with other already existing TRYs from different places in a surrounding area of about 300 km, are used for calculation of the expected energy output of a PVP. A clear increase of energy yield with higher altitude can be observed. The summit Loser (1550 m), with the highest altitude investigated in this study, proved to be the most productive location. Besides the clearer sky and snow reflection, the lower temperature as well as better cooling of the panels by the wind in the Alps contribute to the higher amount of energy output.     

Division of Uzbekistan territory into districts based on complex of meteorological factors affecting the operational efficiency of solar hot water supply systems

Meteorological factors affecting the operational efficiency of the solar hot water supply systems have been considered. The solar radiation incident on the receiver of the solar collector, determined on the basis of observations at the actinometric stations and the calculations of the solar radiation using the data on total and low cloudiness, as well as such characteristics as the outside air temperature, wind velocity, frequency of dust storms and fogs using the data obtained from 32 meteorological stations in the key districts of Uzbekistan were used as the basis for the division into districts.     

Simulation and modeling of solar radiation in Saudi Arabia

A mathematical model is used to generate the hourly data for the total solar radiation on a horizontal surface. The generated data are based on the hourly recorded visibility data for 20 years (1970–1989). The model year technique was then applied to model the 20 years of hourly data of solar radiation into one statistically representative year. A model year of hourly data was then generated for the beam and diffuse components of solar radiation on a horizontal surface. Similarly, a model year of hourly data was also generated for the total solar radiation on tilted surfaces with different orientations with its beam, diffuse and reflected components. A simple methodology is proposed for calculating the solar radiation on vertical surfaces, based on a solar impact factor (SIF). Monthly means and daily totals of hourly sums for each month of the year are discussed. The hourly data of solar radiation for a typical day for each month of the year are presented. The data were generated for the four climatic zones of Saudi Arabia, the hot-dry (Riyadh), the warm-humid (Jeddah), the maritime inland desert climate (Dhahran) and the upland climate zone (Taif). The accuracy of the results is discussed and found to be above 90% representative.     

Prediction of monthly-mean hourly relative humidity, ambient temperature, and wind velocity for India

This paper presents a procedure to predict monthly-mean hourly values of relative humidity, ambient temperature and wind velocity for an Indian location. Three maps, showing distribution of annual-average hourly values of humidity, temperature and wind velocities, are prepared from the analysis of available meteorological data of 205 Indian cities. An equation is obtained for annual-average temperature as a function of altitude of the location. Sets of equations are then developed to predict the said weather parameters by the least square regression analysis of the data of 14 cities, taken from different regions, out of 19 cities for which detailed weather data was available. A ratio of monthly-mean to the yearly-mean value of variable is correlated with month and then hourly to the monthly-mean value is correlated with day-hours. On comparison of the computed results with the measured data of the remaining 5 cities, yearly-average relative standard deviations are 14.6, 10.5 and 26.7% for monthly-mean hourly relative humidity, ambient temperature and wind velocities, respectively.     

Generation of synthetic daily global solar radiation data based on ERA-Interim reanalysis and artificial neural networks

Four variables (total cloud cover, skin temperature, total column water vapour and total column ozone) from meteorological reanalysis were used to generate synthetic daily global solar radiation via artificial neural network (ANN) techniques. The goal of our study was to predict solar radiation values in locations without ground measurements, by using the reanalysis data as an alternative to the use of satellite imagery. The model was validated in Andalusia (Spain), using measured data for nine years from 83 ground stations spread over the region. The geographical location (latitude, longitude), the day of the year, the daily clear sky global radiation, and the four meteorological variables were used as input data, while the daily global solar radiation was the only output of the ANN. Sixty five ground stations were used as training dataset and eighteen stations as independent dataset. The optimum network architecture yielded a root mean square error of 16.4% and a correlation coefficient of 94% for the testing stations. Furthermore, we have successfully tested the forecasting capability of the model with measured radiation values at a later time. These results demonstrate the generalization capability of this approach over unseen data and its ability to produce accurate estimates and forecasts.     

Mapping daily global solar irradiation over Spain: A comparative study of selected approaches

Three methods to estimate the daily global solar irradiation are compared: the Bristow-Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005-2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absolute error 2.33 MJ m⁻² day⁻¹. Daily maps of solar irradiation over Spain at 1-km spatial resolution are produced by applying the ANN method to temperature and precipitation maps generated from ordinary kriging.     

An inexpensive data acquisition system for solar,wind and climatological data recording

A new inexpensive state-of-the-art data-acquisition system, called Suryavijay, for solar radiation, wind and climatological data corresponding to long periods, has been developed and is described in the paper. It acquires data without in situ computers at the field stations. The hourly data are stored in an unpluggable 32 Kbyte EPROM for 24 channel months. The information in the EPROM from a field station is downloaded in a mainframe or personal computer at the mother station. Suryavijay uses a 12 bit analog-to-digital convertor for high resolution and an INTEL 6216 tristat analog multiplexer which accepts differential signals from the sensors and makes the system expandable in integral multiples of 8 channels. It has specially tailor-made signal-conditioning circuits for solar, wind and climatological data channels. In addition, Suryavijay is cost-effective, which makes it possible to have a greater number of field stations in a solar radiation, wind and climatological data network.     

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.     

A software tool for the creation of a typical meteorological year

The generation of a typical meteorological year is of great importance for calculations concerning many applications in the field of thermal engineering. In this context, a method has been proposed by Hall et al. focusing on the generating of typical data, and improving the criterion for final selection of typical meteorological month (TMM). The final selection of the most representative year was done by examining a composite score S, which was calculated as the weighed sum of the scores for the four used meteorological parameters. These parameters were air dry-bulb temperature, relative humidity, wind velocity and global solar radiation intensity.This work reports a new modern software tool using Delphi 6.0 utilizing the Filkenstein–Schafer statistical method for the creation of a typical meteorological year for any site of concern. Whereas, an improved criterion for final selection of typical meteorological month was employed. Such tool allows the user to perform this task without an intimate knowledge of all of the computational details. Using this software tool, a typical meteorological year was generated for Damascus, capital of Syria, as a test run for example. The data processed used were obtained from the Department of Meteorology and cover a period of 10 years (1991–2000).     

Solar radiation maps for Iraq

As measured solar radiation data for all parts of Iraq is not available, it has to be estimated using other weather variables. A number of correlations which use dry bulb temperature, relative humidity and sunshine duration were tried. Finally a correlation using sunshine duration was selected as it gave most accurate estimation of solar radiation. Constants for the correlation for three stations representing three climatic regions in Iraq were determined. Monthly and yearly solar radiation maps were drawn using sunshine duration data from 24 stations from all over the country.     

Experimental and theoretical investigation of global and diffuse solar radiation in the United Arab Emirates

In the present study, the global, direct and diffuse components of solar radiation as well as temperature, relative humidity and wind speed have been continuously monitored and analysed on an hourly, daily and monthly basis. The monthly average daily total solar radiation varies from 2700 W h/m² in December to 8000 W h/m² in June with an average clearness index of 0.65. Experimental data are compared to the predictions of different theoretical models as functions of declination and hour angles. Correlations are obtained describing the variation of hourly, daily and monthly averages of total and diffuse solar radiation using polynomial expressions. Empirical correlations describing the dependence of the daily average diffuse to total radiation ratio on the clearness index are also obtained. Data for the daily diffuse to total radiation ratio are compared to correlations obtained by other investigators. The comparison shows a reasonable agreement with some scatter due to the seasonal dependence of the correlation. Comparison of calculations with experimental measurements under clear sky conditions show excellent agreement with a maximum error of 8%.     

Investigation of the effect of weather conditions on solar radiation in Brunei Darussalam

The amount of solar radiation received on the earth's surface is known to be highly influenced by the weather conditions and the geography of a particular area. This paper presents some results of an investigation that was carried out to find the effects of weather patterns on the solar radiation in Brunei Darussalam, a small country that experiences equatorial climate due to its geographical location. Weather data were collected at a suitable location in the University Brunei Darussalam (UBD) and were compared with the available data provided by the Brunei Darussalam Meteorological Services (BDMS). It has been found that the solar radiation is directly proportional to the atmospheric temperature while it is inversely proportional to the relative humidity. It has also been found that wind speed has little influence on solar radiation. Functional relationships between the solar radiation and the atmospheric temperature, and between the solar radiation and the relative humidity have also been developed from the BDMS weather data. Finally, an artificial neural network (ANN) model has been developed for training and testing the solar radiation data with the inputs of temperature and relative humidity, and a coefficient of determination of around 99% was achieved. This set of data containing all the aforementioned results may serve as a guideline on the solar radiation pattern in the geographical areas around the equator.     

Climate-based empirical model for PV module temperature estimation in tropical environment

The paper proposes new mathematical models to estimating PV module temperature for poly and mono crystalline technologies in tropical climate such as in Malaysia. The developed models are based on measured hourly global solar radiation, ambient temperature, relative humidity, wind speed and module temperature. All data were collected over the year 2009 at GreenTech 92 kWp installed PV system in Selangor, Malaysia. The models were compared using r, MBE, RMSE, and MPE. The results showed that the proposed models give the highest value of correlation coefficient r, and good result when considering statistical indicators i.e. low RMSE, low MBE, and low MPE values. The results show that the proposed regression models have advantages over the conventional approaches for calculating the hourly and day-average PV module temperature, and give the closest results comparing to the actual measurements. The proposed approaches can be used as effective tools for predicting the PV module temperature, whether a simple PV module, open rack system, BIPV installations, or even PV/Thermal collector, in remote and rural locations with no direct measurement equipment. The proposed models can be very useful in studying PV system performance and estimating its energy output.