Clear-sky irradiance predictions for solar resource mapping and large-scale applications: Improved validation methodology and detailed performance analysis of 18 broadband radiative models

The intrinsic performance of 18 broadband radiative models is assessed, using high-quality datasets from five sites in widely different climates. The selected models can predict direct, diffuse and global irradiances under clear skies from atmospheric data, and have all been (or still are) involved in large-scale applications, for instance to prepare solar resource maps and datasets, or to evaluate solar radiation in GIS software. The input data to the models include accurate aerosol and water vapor measurements by collocated sunphotometers, if needed. Cloud occurrences are meticulously scrutinized through the use of various tools to avoid cloud contamination of the test data. The intrinsic performance of the models is evaluated by comparison between their predictions and measurements at high frequency (1-minute time step at four sites, 3-minute at one site). The total expanded uncertainty of these measurements is estimated at 3% for direct irradiance, and 5% for diffuse and global irradiance.Various statistics are calculated to evaluate the systematic and random differences between the data series, as well as the agreement between the cumulative distribution functions. In the latter case, stringent statistics based on the Komolgorov–Smirnov (KS) test are used. Large differences in performance are apparent between models. Those that require more atmospheric inputs perform usually better than simpler models. Whereas many models can predict the global horizontal irradiance within uncertainty limits similar to those of the radiation measurements, the prediction of direct irradiance is less accurate. Moreover, the prediction of diffuse horizontal irradiance is particularly deficient in most models. The cumulative distribution functions also denote areas of concern.A ranking of all models is proposed, based on four statistical indicators: mean bias difference (MBD), root mean square difference (RMSD), total uncertainty with 95% confidence limits (U₉₅), and the newly introduced Combined Performance Index (CPI), which optimally combines two KS indices with RMSD. For direct irradiance, consistently high rankings are obtained with five models (REST2, Ineichen, Hoyt, Bird, and Iqbal-C, in decreasing order of performance) that require a relatively large number of atmospheric inputs. The inferior performance of models requiring little or no atmospheric inputs suggests that large-scale solar resource products derived from them may be inappropriate for serious solar applications. Additionally, prediction uncertainties under ideal clear-sky conditions can propagate and affect all-sky predictions as well—resulting in potential biases in existing solar resource maps at the continent scale, for instance.     

Building a bankable solar radiation dataset

A robust solar radiation dataset is essential for securing competitive financing for solar power projects. The majority of solar radiation datasets are derived from publically available data, though there are an increasing number of proprietary datasets being developed and marketed. Most of these new datasets represent models based on satellite images and validated with ground measured data. This paper focuses on the strengths and weaknesses of the existing publically available solar radiation databases, though the commentary is equally relevant to the newer commercial datasets. While the financing community generally views the solar resource as stable, it also views the material miscalculation of the solar resource as one of the biggest risks in a solar project. Therefore, lenders and rating agencies alike require verification of the solar resource dataset to be utilized at each project location as this translates directly into electric energy production forecasts and revenues. The variability of the solar resource as exhibited by the historical solar data and the accuracy of the dataset play significant roles in estimating the probability of future performance and influences the financial contract that the project is likely to receive.     

A comparison of daily solar radiation estimates for the Northeastern United States using the Northeast Regional Climate Center and National Renewable Energy Laboratory models

Observed solar radiation data at three sites in the northeastern United States are compared with values estimated for nearby airport locations using the National Renewable Energy Laboratory (NREL) and Northeast Regional Climate Center (NRCC) models. A tendency toward considerable overestimation of relatively low values of observed solar radiation is evident in the NREL model. This bias is apparently regardless of season. A similar bias is not detected in the NRCC model. For moderate to high values of solar radiation both models produce estimates with similar accuracy for most practical applications. However, these models both tend to underestimate observed solar radiation on days when near maximum possible radiation levels are received. The tendency for the NREL model to overestimate low solar radiation values appears to be linked to the use of total sky cover, rather than the combination of cloud coverage and cloud base height information. Although total sky coverage data may be superior for estimates of moderate to high daily solar radiation values, it appears that information regarding the height of low overcast layers and the presence of obstructions to visibility, such as fog or haze, is required to accurately estimate low daily solar radiation totals.     

Solar energy scenarios in Brazil,Part one: Resource assessment

The “Solar and Wind Energy Resource Assessment” (SWERA) project was an international project financed by GEF/UNEP, which aimed at providing a consistent and accessible database to foster the insertion of renewable energies on the energy matrix of developing countries. This paper presents the solar energy resource assessment generated during the SWERA project by using the radiative transfer model BRASIL-SR fed with satellite and climate data. The solar irradiation estimates were validated by comparing with the ground data acquired in several sites spread out the Brazilian territory. Maps on 10×10 km² spatial resolution were generated for global, diffuse and direct normal solar irradiation. Solar irradiation on a plane tilted by an angle equal to the local latitude was also generated at the same spatial resolution. Besides the solar resource maps, the annual and seasonal variability of solar energy resource was evaluated and discussed. By analyzing the Brazilian solar resource and variability maps, the great potential available for solar energy applications in Brazil is apparent, even in the semi-temperate climate in the southern region where the annual mean of solar irradiation is comparable to that estimated for the equatorial Amazonian region.     

Temporal variability in direct and global irradiance at various time scales as affected by aerosols

The aerosol optical depth (AOD) is known to be a critical input for radiation modeling purposes, and partially determines the accuracy of modeled direct normal irradiance (DNI) and global horizontal irradiance (GHI). This contribution examines to what extent time variations in AOD also determine the observed variability in DNI, particularly at the daily and longer time scales. Two measures of variability are introduced: the Aerosol Variability Index (AVI) characterizes the magnitude of the variability in AOD over specific periods, from daily to yearly, whereas the Aerosol Sensitivity Index (ASI) relates the magnitude of relative variations in irradiance to absolute variations in AOD. AOD measurements at 180 Aeronet sites over the world are used to obtain clear-sky irradiances with the REST2 radiative model, as well as determinations of ASI and AVI. Large geographic variations exist in AVI, whose largest values are found over western Sahara. The variations of ASI follow a different pattern because it decreases when AOD increases. The variability in GHI is typically 2–4 times lower than that in DNI. On a long-term basis, the normal aerosol-induced variability in DNI is less than ±5% at most sites, but some areas might experience a much larger variability, comparable to that created by large volcanic eruptions. The latest such events predate most current modeled DNI or GHI datasets, making resource assessments potentially too optimistic for bankability if based on such limited data series alone.     

The state of solar energy resource assessment in Chile

The Chilean government has determined that a renewable energy quota of up to 10% of the electrical energy generated must be met by 2024. This plan has already sparked interest in wind, geothermal, hydro and biomass power plants in order to introduce renewable energy systems to the country. Solar energy is being considered only for demonstration, small-scale CSP plants and for domestic water heating applications. This apparent lack of interest in solar energy is partly due to the absence of a valid solar energy database, adequate for energy system simulation and planning activities. One of the available solar radiation databases is 20–40 years old, with measurements taken by pyranographs and Campbell–Stokes devices. A second database from the Chilean Meteorological Service is composed by pyranometer readings, sparsely distributed along the country and available from 1988, with a number of these stations operating intermittently. The Chilean government through its National Energy Commission (CNE) has contracted the formulation of a simulation model and also the deployment of network of measurement stations in northern Chile. Recent efforts by the authors have resulted in a preliminary assessment by satellite image processing. Here, we compare the existing databases of solar radiation in Chile. Monthly mean solar energy maps are created from ground measurements and satellite estimations and compared. It is found that significant deviation exists between sources, and that all ground-station measurements display unknown uncertainty levels, thus highlighting the need for a proper, country-wide long-term resource assessment initiative. However, the solar energy levels throughout the country can be considered as high, and it is thought that they are adequate for energy planning activities – although not yet for proper power plant design and dimensioning.     

Solar resource assessment in the Benelux by merging Meteosat-derived climate data and ground measurements

To characterize the solar radiation in the Benelux countries, a dataset of daily global horizontal solar radiation resulting from both on-site observations time series and long-term satellite-derived data has been generated and analysed at the Royal Meteorological Institute of Belgium (RMI). The developed procedures take advantage of a recently released 23 years long (1983–2005) surface incoming solar radiation (SIS) climate data records derived from the first generation of Meteosat satellites imageries and the radiometric measurements networks operated by the Koninklijk Nederlands Meteorologisch Instituut (KNMI) and RMI, respectively. In addition to the computation of various statistics to quantify the amount and the variability of the solar resources in the Benelux, solar radiation climate zones within the Benelux were defined and the recent trend in solar radiation was characterized.     

Validation of the NSRDB-SUNY global horizontal irradiance in California

Satellite derived global horizontal solar irradiance (GHI) from the SUNY modeled dataset in the National Solar Radiation Database (NSRDB) was compared to measurements from 27 weather stations in California during the years 1998-2005. The statistics of spatial and temporal differences between the two datasets were analyzed and related to meteorological phenomena. Overall mean bias errors (MBE) of the NSRDB-SUNY indicated a GHI overprediction of 5%, which is smaller than the sensor accuracy of ground stations. However, at coastal sites, year-round systematic positive MBEs in the NSRDB-SUNY data up to 18% were observed and monthly MBEs increased up to 54% in the summer months during the morning. These differences were explained by a tendency for the NSRDB-SUNY model to overestimate GHI under cloudy conditions at the coast during summer mornings. A persistent positive evening MBE which was independent of site location and cloudiness occurred at all stations and was explained by an error in the time-shifting method applied in the NSRDB-SUNY. A correction method was derived for these two errors to improve the accuracy of the NSRDB-SUNY data in California.     

Saudi Arabian solar radiation network operation data collection and quality assessment

From 1993 to the present (2000), King Abdulaziz City for Science and Technology (KACST) in Riyadh, Saudi Arabia and the US National Renewable Energy Laboratory (NREL) conducted a joint solar radiation resource assessment project to upgrade the solar resource assessment capability of the Kingdom of Saudi Arabia. KACST has deployed a high quality 12-station network in Saudi Arabia for monitoring solar total horizontal, direct beam, and diffuse radiation. One- and 5-min network data are collected and assessed for quality. 80% or more of the network data fall within quality limits of ±5% for correct partitioning between the three radiation components. We describe the network, quality assessment procedures, data formats and availability.     

Evaluation of conventional and high-performance routine solar radiation measurements for improved solar resource, climatological trends, and radiative modeling

The solar renewable energy community depends on radiometric measurements and instrumentation for data to design and monitor solar energy systems, and develop and validate solar radiation models. This contribution evaluates the impact of instrument uncertainties contributing to data inaccuracies and their effect on short-term and long-term measurement series, and on radiation model validation studies. For the latter part, transposition (horizontal-to-tilt) models are used as an example. Confirming previous studies, it is found that a widely used pyranometer strongly underestimates diffuse and global radiation, particularly in winter, unless appropriate corrective measures are taken. Other types of measurement problems are also discussed, such as those involved in the indirect determination of direct or diffuse irradiance, and in shadowband correction methods. The sensitivity of the predictions from transposition models to inaccuracies in input radiation data is demonstrated. Caution is therefore issued to the whole community regarding drawing detailed conclusions about solar radiation data without due attention to the data quality issues only recently identified.     

Spatial estimation of monthly mean daily sunshine hours and solar radiation across mainland China

Spatial databases of climate data in digital format are required for many agricultural and eco-environmental systems. This study compared 7 approaches for interpolating monthly mean daily sunshine hours and solar radiation over mainland China. The approaches included simple geostatistical approaches to incorporation of Universal Transverse Mercator (UTM) coordinates and elevation. Performance indicators (root mean square error, mean absolute percentage error, and modeling efficiency) showed thin plate smoothing spline with UTM coordinates and elevation (TPS) outperformed other models. Besides, multiple linear regression equations for estimating solar radiation using geographical parameters (UTM coordinates and elevation) and sunshine hours predicted by TPS performed well for the study site. Spatial datasets of annual and monthly mean daily sunshine hours and solar radiation with 1 km resolution were then obtained by the best performance models. Spatial and temporal variability was clearly observed in sunshine hours and solar radiation. For both annual and seasonal scenarios, higher values of sunshine hours and solar radiation existed in north and Tibetan Plateau and lower values were observed in the middle and southern China. Lower values of annual solar radiation were also found in northeastern China. Sunshine hours and solar radiation varied with time, especially from spring to summer and from summer to autumn. The accurate gridded datasets are expected to provide significant information on more efficient use of natural resources.     

Trends in solar radiation in NCEP/NCAR database and measurements in northeastern Brazil

The database from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis project available for the period from 1948 to 2009 was used for obtaining long-term solar radiation for northeastern Brazil. Measurements of global solar radiation (R_s) from data collection platform (DCP) for four climatic zones of northeastern Brazil were compared to the re-analysis data. Applying cluster analysis to R_s from database, homogeneous sub-regions in northeastern Brazil were determined. Long times series of R_s and sunshine duration measurements data for two sites, Petrolina (09°09′S, 40°22′W) and Juazeiro (09°24′S, 40°26′W), exceeding 30 years, were analyzed. In order to exclude the decadal variations which are linked to the Pacific Decadal Oscillation, high-frequency cycles in the solar radiation and sunshine duration time series were eliminated by using a 14-year moving average, and the Mann-Kendall test was employed to assess the long-term variability of re-analysis and measured solar radiation. This study provides an overview of the decrease in solar radiation in a large area, which can be attributed to the global dimming effect. The global solar radiation obtained from the NCEP/NCAR re-analysis data overestimate that obtained from DCP measurements by 1.6% to 18.6%. Results show that there is a notable symmetry between R_s from the re-analysis data and sunshine duration measurements.     

The UK solar energy resource and the impact of climate change

Solar energy use in the UK is increasing dramatically, providing both heat energy and generation of electricity. This trend is expected to continue due to solar technologies becoming cheaper and more readily available along with low carbon government legislation such as the Renewable Heat Incentive (RHI) and Feed in Tariffs (FiTs) supporting solar energy deployment. However, the effects of climate change on the solar resource remain largely unstudied. Climate change affects cloud cover characteristics and consequently directly affects the performance of solar energy technologies.This paper investigates the UK solar irradiation resource for both the present and future climates.The present solar irradiation level was assessed through the conversion of 30 years of observed historical monthly average sunshine duration data. The method and results are validated by comparing the converted solar irradiation levels to actual solar irradiance measurements at weather stations with significant historical records of solar irradiance data.The impact of climate change is investigated across different regions of the UK by using the UKCP09 probabilistic climate change projections.We find that the current average UK annual solar resource is 101.2 Wm⁻², ranging from 128.4 Wm⁻² in the south of England to 71.8 Wm⁻² in the northwest of Scotland. It seems likely that climate change will increase the average resource in the south of the UK, while marginally decreasing it in the Northwest. The overall effect is a mean increase of the UK solar resource, however it will have greater seasonal variability and discrepancies between geographical regions will be reinforced.     

The influence of persistence and variability on the required solar radiation record length

An analysis was made of 17 years of total daily global solar radiation measurements made at St. Paul, Minnesota, a station located in the heart of North America and representative of a continental climate. The analysis was made with the objective of determining the degree of persistence in the solar radiation data and demonstrating the effect of persistence on the statistical analysis of these data.It is concluded that persistence varies through the year and that the characteristic time between independent observations ranges from slightly more than one day to just over three days. Two methods of estimating persistence revealed that some persistence results from daily serial correlation in the solar radiation data and that some persistence is due to interannual variability of monthly mean daily solar radiation.     

Prospects of renewable energy - a feasibility study in the Australian context

Given the recent increasing public focus on climate change issues, there is a need for robust, sustainable and climate friendly power transmission and distribution systems that are intelligent, reliable, and green. Current power systems create environmental impacts as well as contributing to global warming due to their utilization of fossil fuels, especially coal, as carbon dioxide is emitted into the atmosphere. In contrast to fossil fuels, renewable energy is starting to be used as the panacea for solving climate change or global warming problems. This paper describes a feasibility study undertaken to investigate the potentialities of renewable energy including the prospective locations in Australia for renewable energy generation, in particular solar and wind energy. Initially, a hybrid model has been developed to investigate the prospects of wind energy for typical Australian region considering production cost, cost of energy, emission production and contribution from renewable energy using the Hybrid Optimization Model for Electric Renewable (HOMER), a computer model developed by the USA’s National Renewable Energy Laboratory (NREL). This model also explores suitable places around Australia for wind energy generation using statistical analysis. Subsequently, the usefulness of solar energy in the Australian context and suitable locations for solar energy generation are also investigated using a similar hybrid model. Finally, the model has been developed to investigate the prospects of renewable energy in particular wind and solar energy including specific locations in Australia that would be suitable for both wind and solar energy generation. From simulation analysis it is clearly observed that Australia has enormous potentialities for substantially increased use of renewable energy; a large penetration of renewable energy sources into the national power system would reduce CO₂ emissions significantly, contributing to the reduction of global warming.     

A simple and efficient algorithm to estimate daily global solar radiation from geostationary satellite data

Surface global solar radiation (GSR) is the primary renewable energy in nature. Geostationary satellite data are used to map GSR in many inversion algorithms in which ground GSR measurements merely serve to validate the satellite retrievals. In this study, a simple algorithm with artificial neural network (ANN) modeling is proposed to explore the non-linear physical relationship between ground daily GSR measurements and Multi-functional Transport Satellite (MTSAT) all-channel observations in an effort to fully exploit information contained in both data sets. Singular value decomposition is implemented to extract the principal signals from satellite data and a novel method is applied to enhance ANN performance at high altitude. A three-layer feed-forward ANN model is trained with one year of daily GSR measurements at ten ground sites. This trained ANN is then used to map continuous daily GSR for two years, and its performance is validated at all 83 ground sites in China. The evaluation result demonstrates that this algorithm can quickly and efficiently build the ANN model that estimates daily GSR from geostationary satellite data with good accuracy in both space and time.     

Renewable energy resources in Syria

Renewable energy resources in the Syrian Arab Republic are surveyed. Potential of solar, wind and bio-mass resources and their promising applications are analyzed. The annual average long-term solar radiation on a horizontal plane is measured and found to be 5.2 kWh/m² per day. Wind speed measurements were conducted in more than twenty stations spread all over the country. The prospects of these measurements indicate that wind is another promising source of renewable energy in Syria. The registered annual mean daily wind speed in some regions of the country reaches more than 13 m/sec. Theoretical study estimates that the bio-gas production of the daily wastes of humans, animals and agriculture is higher than 300 million cubic meters per year.     

Application of Sayigh “Universal Formula” for global solar radiation estimation in the Niger Delta region of Nigeria

In recent years, renewable energy utilisation in various applications has increased significantly. Applications involving solar thermal energy include air and water heating whilst solar photovoltaic systems have been installed to provide electricity for households in urban and rural areas of the developing economies. The solar radiation data are not easily available for many countries and is therefore estimated most of the times. In this work is presented the results of evaluating the Sayighr “Universal formula” for estimating the global solar radiation in the Niger Delta region of Nigeria with Umudike (longitude 7.33°E, latitude 5.29°N) as a case study. The levels of the global solar radiation which ranged from 1.99 kWh to 6.75 kWh, computed with the method are in agreement with those of earlier authors indicating that the method can be used for reproducing signatures of global solar radiation in the region when actual measurements are not available.     

Irradiance co-spectrum analysis: Tools for decision support and technological planning

Understanding fluctuations of irradiance across spatio-temporal scales is crucial for improving solar resource forecasting and evaluating co-production strategies for solar-fossil power technologies. We show that irradiance can be coupled across spatial and temporal coordinates, for regional analyses of short-term (less than 12 h) and long-term (intra-seasonal) variability as well as inter-site regional coherency and phase behavior. Downwelling shortwave solar irradiance data (DWS; 3-min averaging) is used for a three-year period from 2007 to 2009. Six USA sites are selected from the Integrated Surface Irradiance Study (ISIS) and Surface Radiation (SURFRAD) budget network. Power spectral density is used to analyze the short term and long term variations in DWS. To assess the long-term variations, the data is analyzed in seasonal periods: winter, spring, summer, and fall. Additionally, the cospectra are evaluated to compare the regional variation between sites. The inter-site coherency and phase analysis allows geographic correlation of the solar resource variability to be evaluated. The three pairs of locations include a mid-continent region: Fort Peck, MT with Bismarck, ND; a mid-Atlantic region: Rock Springs, PA with Sterling, VA; and a southwest region: Desert Rock, NV with Hanford, CA. Results indicate that understanding long-term aperiodic oscillations are useful to optimize the co-production of solar/fossil power technologies via slow ramping solutions. Seasonal analysis of short term variations (<12 h) suggests that the ability of a regionally dispersed network of PV to dampen the high variability of solar power production is dependent upon the climatic regime (both location and season-dependent), resulting in a variable decreased demand for fast-ramping fossil technologies.     

Multicriteria GIS modeling of wind and solar farms in Colorado

The majority of electricity and heat in Colorado comes from coal and natural gas; however, renewable energy sources will play an integral role in the state’s energy future. Colorado is the 11th windiest state and has more than 250 sunny days per year. The objectives of this research are to: 1) determine which landcover classes are affiliated with high wind and solar potential; and 2) identify areas that are suitable for wind and solar farms using multicriteria GIS modelling techniques. Renewable potential (NREL wind speed measurements at 50 m above the ground and NREL annual insolation data), landcover, population density, federal lands, and distance to roads, transmission lines, and cities were reclassified according to their suitability. Each was assigned weights based on their relative importance to one another. Superb wind classes are located in high alpine areas. Unfortunately, these areas are not suitable for large-scale wind farm development due to their inaccessibility and location within a sensitive ecosystem. Federal lands have low wind potential. According to the GIS model, ideal areas for wind farm development are located in northeastern Colorado. About 41 850 km² of the state has model scores that are in the 90–100% range. Although annual solar radiation varies slightly, inter-mountain areas receive the most insolation. As far as federal lands, Indian reservations have the greatest solar input. The GIS model indicates that ideal areas for solar development are located in northwestern Colorado and east of Denver. Only 191 km² of the state had model scores that were in the 90–100% range. These results suggest that the variables used in this analysis have more of an effect at eliminating non-suitable areas for large-scale solar farms; a greater area exists for suitable wind farms. However, given the statewide high insolation values with minimal variance, solar projects may be better suited for small-scale residential or commercial projects.