کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6766802 | 512451 | 2016 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Day-ahead resource forecasting for concentrated solar power integration
ترجمه فارسی عنوان
پیش بینی پیش بینی منابع روزافزونی انرژی خورشیدی متمرکز
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
In this work, we validate and enhance previously proposed singe-input direct normal irradiance (DNI) models based on numerical weather prediction (NWP) for intra-week forecasts with over 200,000 hours of ground measurements for 8 locations. Short latency re-forecasting methods to enhance the deterministic forecast accuracies are presented and discussed. The basic forecast is applied to 15 additional locations in North America with satellite-derived DNI data. The basic model outperforms the persistence model at all 23 locations with a skill between 12.4% and 38.2%. The RMSE of the basic forecast is in the range of 204.9Â WÂ mâ2 to 309.9Â WÂ mâ2. The implementation of stochastic learning re-forecasting methods yields further reduction in error from 204.9Â WÂ mâ2 to 176.5Â WÂ mâ2. To a great extent, the errors are caused by inaccuracies in the NWP cloud prediction. Improved assessment of atmospheric turbidity has limited impact on reducing forecast errors. Our results suggest that NWP-based DNI forecasts are very capable of reducing power and net-load uncertainty introduced by concentrated solar power plants at all locations in North America. Operating reserves to balance uncertainty in day-ahead schedules can be reduced on average by an estimated 28.6% through the application of the basic forecast.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 86, February 2016, Pages 866-876
Journal: Renewable Energy - Volume 86, February 2016, Pages 866-876
نویسندگان
Lukas Nonnenmacher, Amanpreet Kaur, Carlos F.M. Coimbra,