This work focuses on the performance comparison of monocrystalline and polycrystalline Si solar photovoltaic(SPV)modules under tropical wet and dry climatic conditions in east-central India(21.16°N 81.65°E,R...
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This work focuses on the performance comparison of monocrystalline and polycrystalline Si solar photovoltaic(SPV)modules under tropical wet and dry climatic conditions in east-central India(21.16°N 81.65°E,Raipur,Chhattisgarh).This study would help to select the SPV module for system installation in the east-central part of the *** comparative analysis,we used performance ratio(PR)and efficiency as figures of *** plane-of-array(POA)irradiance was used to determine the efficiency of the *** decomposition and transposition models calculated the POA values from the measured global horizontal *** data were analysed systematically for 6 months in the non-rainy season,from October 2020 to March *** attention was given to solar irradiance,ambient temperature and module temperature-the parameters that affect the performance of PV *** month of October showed the highest variation in irradiance and *** highest average module temperatures(51-52℃)were observed in October-November,while the lowest average module temperatures(34℃ for mono-Si and 36℃ for poly-Si)were observed in *** highest value of average monthly POA irradiance(568 W/m^(2))was observed in February and the lowest(483 W/m^(2))in *** results showed that the monocrystalline SPV module performed better than the polycrystalline module under all weather *** maximum observed values of mono-Si and poly-Si panel PRs were 0.89 and 0.86,respectively,in December.
There are several sensors available in the market to measure the plane-of-array irradiance for photovoltaic applications. The prices of these sensors vary according to the design, calibration procedure, and conducted ...
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There are several sensors available in the market to measure the plane-of-array irradiance for photovoltaic applications. The prices of these sensors vary according to the design, calibration procedure, and conducted characterization. In this article, two types of silicon-based sensors with and without temperature correction capabilities are compared with a high-accuracy thermopile pyranometer to check their performance. The obtained results showed that silicon-based sensors deviate from the output of the pyranometers. The tested silicon-based pyranometers overestimate the irradiance with the median bias deviations of around 1.43% (with the average measured irradiance of 256 W/m(2)). For temperature-corrected silicon pyranometer, the bias deviation is 0.07% with the deviation range of -6.5%-10% (with the average measured irradiance of 257 W/m(2)). A working-class reference cell was also tested, resulting in a bias deviation of -1.74% and the deviation range of -13%-7% (with the average measured irradiance of 304 W/m(2)). The effect of air mass on the performance of cost-effective sensors was additionally analyzed. Within the measurement time window, the result also showed that for the silicon-based sensors under tests, the effects of the environmental conditions have the following qualitative order of influence: angle of incidence > red-shift > temperature. The performance of silicon-based sensors also showed seasonal dependence, being more accurate during summertime and wintertime, respectively, for the silicon pyranometer and the working-class reference cell. Finally, using the statistical evaluation, simple linear correction functions are introduced for silicon-based sensors.
Using deployed PV generation as inputs for spatio-temporal forecasting approaches has the potential for fast and scalable very short-term PV forecasting in the urban environment but one has to consider the effect of t...
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Using deployed PV generation as inputs for spatio-temporal forecasting approaches has the potential for fast and scalable very short-term PV forecasting in the urban environment but one has to consider the effect of their tilt and orientation on the forecasting accuracy. To address this issue, tilted irradiance data sets were simulated using state of the art solutions on a horizontal irradiance data set from a pyranometer network deployed in Oahu, Hawaii, and used as inputs to train a 10-s ahead linear ARX model. Results showed that the mismatch in tilt/orientation degrades the forecast skill, justified by the difference in the diffuse fraction of each surface and, thus, how each reacts to changes in cloud cover. From 4000 simulated sets, it was shown that using information from more sites led to better forecasts and made the model performance less sensitive to the PV modules' tilt and orientation. Forecast skill showed to be quite sensitive to the tilt and orientation ensemble when the inputs consisted of only rooftop or facade systems (between 18.1-29.6% and 8.2-19.4%, respectively). Forecasting a rooftop system with vertically tilted neighbors lead to considerably lower skill values (9.8-16.2%) and benefitted when all shared the same orientation. On the other hand, forecasting a vertically tilted system with rooftop neighbors had a lower impact (9.2-14.7%) and benefitted from diversely oriented neighbors.
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