PV Yield Research Articles

Energy harvesting potential for 3 EVs equipped with PV for the area of Graz in Austria

Vehicle-integrated photovoltaics present a plausible solution to address electric vehicle barriers of limited charging access and driving ranges. This work demonstrates a simulation tool that encompasses influencing factors on a vehicle's PV yield by comparing geographical areas, vehicle classes, vehicle surfaces, weather conditions, driving/parking cycles, shading scenarios, and electric configurations. The results quantify the increase in battery energy and vehicle mileage. Extensive simulations show the roof is the primary zone for PV integration, whereas the doors contribute greatly in winter. Using high-efficiency PV modules on the roof can supply over 20 % of monthly mileage from March to September. On average, 25 % of 1000 km monthly mileage can be covered with full-vehicle PV coverage. This drops to 16 % when excluding PV doors. This technology's success centers on lightweight vehicles with large surfaces and high-efficiency modules exceeding 20 %. Simulations show that a 10 % increase in efficiency reduces charging energy needs by 12–16 %.

Experimental energy performance assessment of a bifacial photovoltaic system and effect of cool roof coating

In the quest for high albedo materials that boost the energy production of bifacial photovoltaic systems, a range of material already exists for reducing building roof surface temperatures, called cool roof materials. However, there is a noticeable absence of scientific literature addressing the combination of cool roofs and bifacial photovoltaic systems. This study investigates the photovoltaic performance of a bifacial photovoltaic system with cool roof coating on the underside and its impact on floor temperature. For this purpose, four ∼1kWp prototypes were installed on the terrace of the GAIA building of the UPC near Barcelona, Spain: (1) bifacial panels above a cool roof, (2) bifacial panels above normal floor, (3) bifacial panels above a normal floor with n-type solar cells encapsulated in TPO, and (4) monofacial panels. The results reveal 8.6 % higher PV yield for bifacial with cool roof compared to monofacial, and 4–4.5 % higher for bifacial (normal floor) compared to monofacial. Additionally, the cool roof coating contributes to reducing the floor temperatures, particularly in the unshaded (exposed) areas during summer (−3.8 °C). The presence of photovoltaic panels has also demonstrated a positive impact on floor temperatures during both winter and summer. Thus, the cool roof coating offers two benefits: increased photovoltaic yield and reduced building cooling requirements, both of which are associated with economic advantages. The cool roof coating can be integrated into existing or new bifacial roof systems.

Smart energy monitoring and power quality performance based evaluation of 100-kW grid tied PV system

Globally, the demand for energy from renewable sources is growing due to the increasing electricity consumption and the pollution of fossil fuels. The government has framed various policies to facilitate green energy generation, encouraging renewable energy source usage through PV installations in multiple sectors, including educational institutions. The primary objective of this paper is to propose a methodological approach for analysing the performance of the installed PV system on the rooftop of a university building in Tamil Nadu, India. The site selected is favourable for electricity generation from PV systems with an average global solar radiation of 5.82 kWh/m2day. Solar energy changes periodically with annual and daily variations and is not steady due to seasonal changes. The step-by-step performance assessment and the annual performance of the 100-kW solar PV system, which was instituted in 2019, with the forecasted parameters, are presented in this paper. Therefore, the assessment analysis is carried out in four phases: feasibility assessment, Energy yield assessment, Life cycle assessment, and Power quality assessment. To improve the solar PV output and efficiency, considering the solar irradiation, temperature, wind velocity, etc., PV yield is measured to evaluate the PV system's energy metrics. This paper also considers the carbon credits earned, solar power generated in the location, and the payback period. The power quality assessment is carried out in this paper to test the PV plant's compliance with effective grid integration.

Computational intelligence based PEVs aggregator scheduling with support for photovoltaic power penetrated distribution grid under snow conditions

• A snow loss-aware deep learning-based short-term PV yield predictor is developed. • Stochastic day-ahead scheduling of an independent PEVs aggregator is investigated. • A novel local balancing service offered by the PEVs aggregator to DSO is proposed. • The service benefits PEV owners through providing extra energy demand of the grid. This paper addresses the issue of optimal day-ahead scheduling of a plug-in electric vehicles (PEVs) aggregator that participates in the electricity market and offers an out-of-market balancing service to the local renewable power penetrated distribution system in a snow-prone area. The proposed balancing service provides a reliable source of flexibility for the extra real-time energy demand of the distribution system operator (DSO) which originates from the difference between its day-ahead bids and the actual demand. The problem is investigated on a snowy day when the DSO's day-ahead decisions encounter more uncertainty due to the considerable effect of snow loss on the DSO's photovoltaic plant performance. The aggregator's scheduling is formulated as two-stage stochastic programming which minimizes the PEVs’ charging cost. Monte Carlo simulation and K-means clustering are implemented to generate scenarios of driving patterns and real-time energy market prices, respectively. Offering the balancing service requires day-ahead predictions of the photovoltaic power and the grid load demand which are modeled using long short-term memory networks. The problem is formulated as mixed-integer linear programming. The results show that the proposed scheduling approach reduces the PEVs’ charging cost by 53% and guarantees the grid normal operation. Moreover, the balancing service can reduce the expected PEVs’ charging cost and the DSO's real-time cost by 12% and 14%, respectively. © 2017 Elsevier Inc. All rights reserved.

Performance variability and implications for yield prediction of rooftop PV systems – Analysis of 246 identical systems

While the performance of PV systems and its associated uncertainties are well understood for standard test conditions in the laboratory, there is still limited knowledge about the magnitude and mechanism of the PV performance variability under actual operating conditions. This paper aims to identify the performance variability between identical rooftop PV systems in the field and formulate risk mitigation strategies to reduce the error of annual yield prediction. To achieve the aim, long-term monitoring data of 246 identical rooftop PV systems in 19 sub-urban residential communities is analyzed. Through structured side-by-side comparisons, the mechanism of PV performance variations linked to location, module orientation, season, sky clearness, and system age is investigated. It is found that PV performance variability increases in the real built environment compared with the nameplate bandwidth declared by the manufacturers. Significant variation of PV operating performance is observed not only between different locations, but also between peer systems in the same neighborhood. Even in low-rise sub-urban settings, local shading and masking effects play a prominent role and can introduce great uncertainties. Due to the site-to-site and peer-to-peer uniqueness of PV performance, it is inappropriate to employ an identical empirical derate value for all cases. Commissioning and monitoring of PV systems in the field for at least one month with the largest range of solar elevation can significantly reduce PV yield prediction error and mitigate financial risks.

High-transparency Clear Glass Windows with Large PV Energy Outputs

Multiple modern glass and window products based on novel glazing designs, metal-dielectric coatings, and proprietary interlayer types have been developed recently. Advanced windows of today can control properties such as thermal emissivity, heat gain, colour, and transparency. In more recent and more novel glass products, solar energy harvesting through PV integration is also featured. Typically, semitransparent and also highly-transparent PV windows are purpose-designed, to include luminescent materials, special microstructures, and customized electric circuitry. Recently, significant progress has been demonstrated in building integrated highly-transparent solar windows (VLT up to 70%, with Pmax ~ 30-33 Wp/m2, eg Clearvue PV Solar Windows); these are expected to add momentum towards the development of smart cities. These Clearvue window systems are, at present in 2021, the only type of high-transparency and clear construction materials capable of providing significant energy savings in buildings, simultaneously with renewable energy generation. The technology has already been deployed and tested in both commercial property applications and in R&D greenhousing. Of special interest is the combination of properties provided by Clearvue solar window products, which includes significant power conversion efficiency (~3.3%), which is achieved in windows of colour rendering index of 99%, simultaneously featuring high PV Yield in multi-oriented building-integrated PV (BIPV) installations.

Evaluation of High-Resolution Satellite-Derived Solar Radiation Data for PV Performance Simulation in East Africa

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data are needed to ascertain to what extent these can satisfy the needs of local people. In this paper, we investigate how to choose the most accurate satellite-derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data are needed to model the output of solar cooking systems, such as a solar panel, battery and electric pressure cooker. Four easily accessible global horizontal irradiation (GHI) satellite databases are validated against ground measurements using a range of statistical tests. The results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to the appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

Calculating solar irradiance without shading geometry: a point cloud-based method

Accurate simulation of solar irradiance on facades and roofs in a built environment is a critical step for determining photovoltaic yield and solar gains of buildings, which in an urban setting are often affected by the surroundings. In this paper, a new modelling method is introduced that uses Digital Surface Model (DSM) point clouds as shading geometry, combined with a matrix-based approach for simulating solar irradiance. The proposed method uses the DSM as shading geometry directly, eliminating the need for 3D surface geometry generation and conducting ray-tracing, thus simplifying the simulation workflow. The real-world applicability is demonstrated with two case studies, where it is shown that if the site is shaded, ignoring the shading from the surroundings would cause overprediction in the simulated annual PV yield, underprediction of the annual heating and overprediction of the annual cooling demand.

Novel Python-based “ all-regressor model ” application for photovoltaic plant-specific yield estimation and systematic analysis

ABSTRACT The deployment of photovoltaic technology in global electricity generation has reduced the dependence on fossil fuels and the adverse effect of global warming climate changes. However, the variability of PV output due to its reliance on meteorological parameters hinders the total maximization of the technology. In this paper, the interdependence of meteorological parameters on accurate estimation of the specific yield of the PV system is examined using machine learning algorithms. The analysis uses 2 years of meteorological data in a 10-min interval. The meteorological parameters used as input are global solar radiation (GR), wind speed (WS), wind direction (WD), the standard deviation of wind direction (WSD), air temperature (AT), and relative humidity (RH), while the output variable is the PV specific yield (PVSY). This study uses a Python-based “all regressor” machine learning algorithm, to analyze their accuracy for the estimation analysis at different input feature space. The accuracy of the models and algorithms is analyzed using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of correlation (R2). The best performance in the two feature space was retrieved for AT, and GR with MSE and MAE values of 0.113 and 0.252, respectively. The best performance in the three feature space was retrieved for WSD, AT, and GR with MSE and MAE values of 0.109 and 0.255, respectively. The results show that the most accurate PVSY estimation was for the four input feature space with input variables of WS, AT, GR, and RH. The K-nearest neighbor (KNN) proved to be the most appropriate algorithm for this feature space with an RMSE and MAE score of 0.101 and 0.266, respectively. The study also shows that the most significant meteorological inputs in PV yield estimation are air temperature and global solar radiation. The study also noted that a large feature space adds uncertainties to the training process. Finally, the paper concludes that the sunshine-based parameters are key inputs for high-quality PV yield estimation regardless of the number of input combinations. It is also concluded that, in this study, the most accurate regression algorithms for estimation are the Least Absolute Shrinkage and Selection Operator (LASSO), KNN, Elastic Net (EN), Decision Tree regressor (DT), and Extra Tree regression (ET).

Estimation of an Optimal PV Panel Cleaning Strategy Based on Both Annual Radiation Profile and Module Degradation

This paper addresses a methodology to optimize PV panel cleaning schedule. PV system yield is impacted due to soiling on panel surface. This soiling changes during the system operation as a consequence of environmental conditions. In order to reduce the impact of soiling accumulation, the panel surface must be cleaned. However, cost and frequency of cleaning can affect plant revenue. Therefore, to enhance financial performance, an optimal cleaning strategy must be selected. This strategy depends mostly on energy cost, soiling rate, cleaning costs, and system efficiency. Hence, the model proposed in this work takes into account these variables in order to support the decisions during the assessment of PV yield. The methodology was evaluated through two irradiation profiles with different associated costs. As a result, the methodology made it possible to estimate a cleaning strategy optimizing the incomes under different scenarios.

Investigation of PV yield differences in a desert climate

PV modules which are less sensitive to temperature (i.e. have lower power temperature coefficient, PTC) are expected to produce more energy in hot environments. Yet a year-long study of 26 PV technologies in Doha, Qatar, found that their daily Specific Yield (Wh/day/Wp) only weakly correlated with their PTC. To investigate possible explanations, we examined in detail the field data from one multi-Si and one CdTe string, and calculated their energy losses attributable to module temperature, low-light conditions, and “Wp discrepancy” (difference in Wp between manufacturers’ flash tests and field measurements). We found that although the CdTe system had lower PTC than multi-Si, it had larger Wp discrepancy, which largely accounted for its lower Specific Yield. Further, PTC values derived from field measurements were significantly different from the modules’ nameplate values, for both the multi-Si and CdTe systems. The results indicate that modules’ nameplate PTC values did not strongly predict their relative Specific Yields in desert climates, largely because of discrepancy between nominal and field values of PTC and Wp.

CMIP-5 models project photovoltaics are a no-regrets investment in Europe irrespective of climate change

Using projections of surface solar radiation and temperature from 23 CMIP5 global climate models for two climate change scenarios (RCP4.5 & 8.5) we quantify the average change in PV electricity production expected in the years 2060–2080 compared to the present (2007–2027). We upsample daily radiation data to hourly resolution with a sinusoidal diurnal cycle model and split it into direct and diffuse radiation with the semi-empirical BRL model as input to a PV electricity generation model. Locally, changes in PV potential from −6% to +3% in annual and −25% to +10% in monthly means are shown. These projections are combined with a PV deployment scenario and show countries benefitting from increased PV yields include Spain, France, Italy and Germany. We also calculate uncertainties when calculating PV yield with input data at daily or lower resolution, demonstrating that our method to derive synthetic hourly profiles should be of use for other researchers using input data with low temporal resolution. We conclude that PV is an attractive and no-regrets investment in Europe irrespective of future climate change, and can continue to play a key role in energy system decarbonisation.

MP28-18 INHERITED PATHOGENIC VARIANTS IDENTIFIED IN PATIENTS WITH RENAL CANCER

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging I1 Apr 2018MP28-18 INHERITED PATHOGENIC VARIANTS IDENTIFIED IN PATIENTS WITH RENAL CANCER Maegan Roberts, Megan Marshall, Natalie Carter, Kevin Arvai, Lance Grau, Shirley Yao, Ying Wang, Rachel Klein, and Kathleen Hruska Maegan RobertsMaegan Roberts More articles by this author , Megan MarshallMegan Marshall More articles by this author , Natalie CarterNatalie Carter More articles by this author , Kevin ArvaiKevin Arvai More articles by this author , Lance GrauLance Grau More articles by this author , Shirley YaoShirley Yao More articles by this author , Ying WangYing Wang More articles by this author , Rachel KleinRachel Klein More articles by this author , and Kathleen HruskaKathleen Hruska More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.918AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES It is estimated that 3-5% of all renal cancers are due to a hereditary predisposition. Additionally, hereditary renal cancers have been reported to develop at earlier ages and are more likely to present as multifocal or bilateral disease. We describe clinical features of probands with renal cancer with a pathogenic or likely pathogenic variant (collectively, PV) identified via multi-gene hereditary cancer panel testing. METHODS We performed a retrospective review of clinical and molecular data for all individuals diagnosed with renal cancer undergoing multi-gene panel testing at our clinical diagnostic laboratory (N=945). Individuals with cancer of the renal pelvis were excluded. Student′s t-test with two-sided p-value of 0.05 was used to compare age at diagnosis. RESULTS Of the 945 individuals reporting a personal history of renal cancer, 93 PVs were identified in 89 individuals (9.4%). Thirty-nine individuals (4.1%) were identified as having a PV in a gene previously associated with hereditary renal cancer. While 50 individuals (5.3%) were found to have a PV in a gene not previously associated with hereditary renal cancer, 39 (78%) reported a history of a second non-renal primary tumor. A personal history of >1 renal cancer was reported for 98 (10.4%) individuals. Among these, 12 (12.2%) were found to have a PV in ATM, BAP1, CHEK2, FH (3), MET, MITF, WT1, SDHA, TSC1, and VHL. Individuals with >1 renal cancer who tested positive (41.2 yrs) tended to be diagnosed at a younger age compared to those who tested negative (49.8 yrs) (p=0.085). Among those reporting a single primary renal cancer, the yield was 7.4% (21/284), and the average age at diagnosis was not significantly different between those testing positive (42.0 yrs) and those testing negative (44.2 yrs) (p=0.544). When assessing family history, 178 (18.8%) reported a family history of renal cancer; 20 (11.2%) were found to have a PV. CONCLUSIONS The overall PV yield observed in this cohort was 9.4%. When limiting to genes known to be associated with a hereditary predisposition to renal cancer, the yield was 4.1%, consistent with published estimates. The highest PV yield (12.2%) was observed in individuals with >1 renal tumor followed by those reporting a family history of renal cancer (11.2%). Age at diagnosis was not significantly different with regards to PV yield in either group evaluated. Overall, individuals with >1 renal tumor or a family history of renal cancer have the highest yield for PVs. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e363-e364 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Maegan Roberts More articles by this author Megan Marshall More articles by this author Natalie Carter More articles by this author Kevin Arvai More articles by this author Lance Grau More articles by this author Shirley Yao More articles by this author Ying Wang More articles by this author Rachel Klein More articles by this author Kathleen Hruska More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

RETRACTED: Calculating potential of solar energy and CO2 emissions reduction for cityscale buildings based on 3D remote sensing technologies

Solar energy utilization will play an increasingly important role in sustainable urban development because it will help motivate low-carbon development. This study proposed a framework that calculates the solar irradiance on roofs and facades of city-scale buildings, as well as the potential reductions in energy consumption and CO2 emissions that can be derived from solar energy utilization. This framework consolidates data acquired using 3D remote sensing techniques (e.g., light detection and ranging (LiDAR) data and digital photogrammetry) with existing 3D building vector models from urban planning and building design. To further the economical use of solar energy, the framework provides a new strategy based on three aspects (threshold, structure, and orientation), for a more realistic analysis of the available area of roofs and facades, by combining street view images and point clouds. Nanjing, China, was selected as a study area for validating and analyzing the proposed framework. The experimental area covers approximately 30 km² and 5216 buildings. The reliability of the proposed method was validated by comparing the results with the computation modules of mainstream commercial programs and previous international studies. The following conclusions were drawn from the experimental area. (1) Annual solar energy on the roofs of all buildings was 9647.89 GWh, corresponding to a PV yield of 1447.18 GWh, which equates to reductions of 584.66 kt in standard coal consumption and 1261.94 kt in CO2 emissions. The annual solar energy on the facades was 5364.63 GWh, corresponding to a PV yield of 804.69 GWh, which equates to reductions of 325.10 kt in standard coal consumption and 701.69 kt in CO2 emissions. (2) Based on the calculated potential solar energy utilization of the building envelopes, it was shown that the utilization of solar radiation on roofs and facades would promote low-carbon urban development. The utilization of solar energy on the facades of high buildings will contribute to the supply of energy for these buildings and help balance their energy consumption. (3) As the solar incidence angle changes over time, the reductions in energy consumption and CO2 emissions related to PV power generation are maximal in summer for building roofs, whereas no significant seasonal differences were observed for facades. (4) Incorporating the available area translates the utilization of solar energy and potential CO2 reductions from theory to practical application by reducing inefficient and invalid areas. The percentages of available area of roof and facade were estimated at 47% and 16% by comprehensive calculations considering three aspects (threshold, structure, and orientation). The corresponding potential of solar energy on the roofs and facades in the validation area B are 167.18 GWh and 53.63 GWh, respectively, compared to the original theoretic values of 316.97 GWh and 237.98 GWh.

出力予測を考慮した発電機起動停止計画モデルによる太陽光発電出力制御必要量の分析

High penetration of variable renewable energy (VRE) systems will impact power system operations because generation of VRE systems depends on weather conditions. We analyzed the operational situation and photovoltaic power yield curtailment for the Tokyo Electric Power Company area in 2030 using day-ahead unit commitment (UC) model when a high penetration of VRE systems is expected. At first, we updated our UC model to optimize PV curtailment keeping power system's security (load frequency control reserve) and minimizing operational cost. Using the updated model, we found that in case of 53GW and 100GW of PV penetration, around 0.34% and 4.83% of PV curtailment would be required throughout the year. High curtailment rate would be required only in spring and autumn seasons when power demand is small and PV yield is large. Regarding the other seasons, high curtailment rate would not be needed in any of the high PV penetration scenarios.

Potential of distributed photovoltaics in urban Chile

In this article, we present an updated potential of distributed photovoltaics in cities in Chile, which is one of the most promising regions for solar energy utilization because of high insolation, high energy costs and rapid urbanization. The study is based on detailed analysis of the spatio-temporal solar potential including load matching in a southern Chilean city, which is then extrapolated to all cities with more than 15,000 inhabitants. In addition, we estimate the cost-effectiveness of such solar city schemes. Our results show that in the city of Concepción PV could cover up to 90% of the annual electricity demand. On national scale, cities could on average produce 83% of their annual electricity demand, but in the northern part this could exceed even 100%. If scaling the PV yield from distributed PV in cities to whole Chile that would correspond to 22% of country’s electricity demand. The cost (LCOE) of PV in cities seems to be cost-effective against grid electricity, i.e. in the Chilean context PV has already reached grid-parity in cities.

Probabilistic analysis of solar photovoltaic self‐consumption using Bayesian network models

In order to assess the systemic value and impacts of multiple PV systems in urban areas, detailed analysis of on-site electricity consumption and of solar PV yield at relatively high temporal resolution is required, together with an understanding of the impacts of stochastic variations in consumption and PV generation. In this study, measured and simulated time series data for consumption and PV generation at 5 and 1 minute resolution for a large number of domestic PV systems are analysed, and a statistical evaluation of self-consumption carried out. The results show a significant variability of annual PV self-consumption across the sample population, with typical median annual self-consumption of 31% and inter-quartile range of 22-44%. 10% of the dwellings exceed a self-consumption of 60% with 10% achieving 14% or less. The results have been used to construct a Bayesian Network model capable of probabilistically analysing self-consumption given consumption and PV generation. This model provides a basis for rapid detailed analysis of the techno-economic characteristics and socio-economic impacts of PV in a range of built environment contexts, from single building to district scales.

Forecast study of the supply curve of solar and wind technologies in Argentina, Brazil, Chile and Mexico

This paper forecasts the supply curve of non-conventional renewable technologies such as wind and solar generating stations in Argentina, Brazil, Chile and Mexico using technological and economic parameters. It also estimates the additional investment costs in solar and wind generation for reaching the renewable energy target in each of these countries. To assess the power supply profile from 1 axis tracking PV and horizontal axis wind turbine (three blade) stations, two different scenarios are developed for 2014 and 2025. Scenario 1 estimates the PV and wind annual electricity yield by using polycrystalline silicon (cSi poly) as semiconductor material for PV cells and a Vestas 90–3.0 MW turbine for the wind for 2014.Scenario 2 assumes a more efficient technology, such as CPV. In fact, the model employs 45% efficiency triple junction cells using ∼3500 m2 for each 1 MW installed capacity in 2025. Moreover, this scenario also assumes a more powerful type of turbine, i.e. Vestas 112–3.075 MW. The biggest potential for wind power is found to be in Argentina, followed by Brazil, Mexico and Chile. In addition, a 550 MW installed capacity CPV power station, using triple junction cells could generate up to 4 TWh in Chile in 2025.

Photovoltaic yield: correction method for the mismatch between the solar spectrum and the reference ASTMG AM1.5G spectrum

We propose a method for a spectral correction of the predicted PV yield and we show the importance of the spectral mismatch on the solar cell. Indeed, currently predicted PV yield are made considering solar irradiation, ambient temperature, incidence angle and partially (or not) the solar spectrum. However, the solar spectrum is not always the same. It varies depending on the site location, atmospheric conditions, time of the day...This may impact the photovoltaic solar cells differently according to their technology (crystalline Silicon, thin film, multi-junctions...) This paper presents a method for calculating the correction of the short-circuit current of a photovoltaic cell due to the mismatch of the solar spectrum with the reference ASTM AM1.5G spectrum, for a specific site, throughout the year, using monthly data of AERONET (AErosol RObotic NETwork established by NASA and CNRS) and the model SMARTS (simple model for atmospheric transmission of sunshine) developed by the NREL. We applied this correction method on the site of Palaiseau (France, 48.7°N, 2.2°E, 156 m), close to our laboratory, just for comparison and the example of Blida (Algeria, 36°N, 2°E, 230 m) is given for one year. This example illustrates the importance of this spectral correction to better estimate the photovoltaic yield. To be more precise, instead of modeling the solar spectral distribution, one can measure it with a spectro-radiometer, and then, derive the spectral mismatch correction. Some of our typical measurements are presented in this paper.

Awareness Among Young Architect and Photovoltaic Potential in Malaysia

The paper discusses the awareness of photovoltaic technology among the young architect in Malaysia. A survey has been done to know how much their knowledge about the photovoltaic technology. It also discusses the main problem to design with BIPV. Furthermore, this paper highlight the PV potential in Malaysia on how PV can reduce the electricity demand in a building, what are the benefit from extra PV yield and also PV as the environmental friendly technology.