کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
13421903 | 1841705 | 2020 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Novel stochastic methods to predict short-term solar radiation and photovoltaic power
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Novel stochastic methods to predict short-term solar radiation and photovoltaic power Novel stochastic methods to predict short-term solar radiation and photovoltaic power](/preview/png/13421903.png)
چکیده انگلیسی
Solar forecasting has evolved towards becoming a key component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents two novel stochastic forecasting models for solar PV by utilizing historical measurement data to outline a short-term high-resolution probabilistic behavior of solar. First, an uncertain basis functions method is used to forecast both solar radiation and PV power. Three possible distributions are considered for the uncertain basis functions - Gaussian, Laplace, and Uniform distributions. Second, stochastic state-space models are applied to characterize the behaviors of solar radiation and PV power output. A filter-based expectation-maximization and Kalman filtering mechanism is employed to recursively estimate the system parameters and state variables. This enables the system to accurately forecast small as well as large fluctuations of the solar signals. The introduced forecasting models are suitable for real-time tertiary dispatch controllers and optimal power controllers. The PV forecasting models are tested using solar radiation and PV power measurement data collected from a 13.5â¯kWâ¯PV panel installed on the rooftop of our laboratory. The results are compared with standard time series forecasting mechanisms and show a substantial improvement in the forecasting accuracy of the total energy produced.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 145, January 2020, Pages 333-346
Journal: Renewable Energy - Volume 145, January 2020, Pages 333-346
نویسندگان
Jin Dong, Mohammed M. Olama, Teja Kuruganti, Alexander M. Melin, Seddik M. Djouadi, Yichen Zhang, Yaosuo Xue,