کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5450883 | 1513061 | 2017 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Modeling of photovoltaic soiling loss as a function of environmental variables
ترجمه فارسی عنوان
مدل سازی از دست دادن خاک فتوولتائیک به عنوان عاملی از متغیرهای زیست محیطی
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
In this study, an artificial neural network (ANN) approach was applied for modeling the relation between environmental variables and daily change in the Cleanness Index (ÎCI, a measure of performance loss due to soiling) of photovoltaic modules in the field in Doha, Qatar. The daily ÎCI was examined among a number of three-dimensional intervals of the daily mean of the environmental variables (i.e., the intervals of two environmental variables were presented on x and y dimensions, and average values of daily ÎCI on the third dimension), in order to qualitatively establish the relations that might help to develop improved PV soiling prediction models. Then, an ANN-based model was set up to simulate the relationship between daily ÎCI and environmental variables and compared with a linear regression model, both models using the same input variables, including present day and previous day environmental conditions, and cumulative exposure time. Strong interactions were observed among environmental variables PM10, relative humidity (RH) and wind speed (WS) regarding their effect on the daily ÎCI. Overall, higher PM10 resulted in more negative daily ÎCI (i.e. the module became more soiled), and this effect was more visible at low WS and RH levels, but at high WS (>4 m sâ1) and high RH (>65%) levels, PM10 had no significant (p > 0.05, two tailed t-test) effect on daily ÎCI. Mostly, WS and RH determined how much airborne dust accumulates on the module surfaces and thereby affects the output of the PV modules. Higher WS typically favored more positive daily ÎCI when RH was low, but at higher RH levels (>50%) daily ÎCI was more likely to be negative with increasing WS. In fact, high RH levels were related to negative daily ÎCI only at higher WS levels (>2 m sâ1); at lower WS levels RH had no significant effect on daily ÎCI. These effects were apparently due to the deposition-resuspension mechanisms of dust accumulation on the PV panel surfaces. The ANN model performed significantly better in predicting daily ÎCI as well as cumulative CI than the linear model in term of R2 values and statistical error indexes. The previous day environmental conditions had a significant effect on the modeling outcome. The inclusion of the wind gustiness and cumulative exposure time also considerably improved the model prediction capability. The advantage of the ANN-based model is its simplicity, ease of data fitting and no requirement of an accurate mathematical model.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Solar Energy - Volume 157, 15 November 2017, Pages 397-407
Journal: Solar Energy - Volume 157, 15 November 2017, Pages 397-407
نویسندگان
Wasim Javed, Bing Guo, Benjamin Figgis,