کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
8072863 1521434 2016 15 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی انرژی (عمومی)
پیش نمایش صفحه اول مقاله
Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function
چکیده انگلیسی
Highly accurate short-term power load forecasting (STLF) is fundamental to the success of reducing the risk when making power system planning and operational decisions. For quantifying uncertainty associated with power load and obtaining more information of future load, a probability density forecasting method based on quantile regression neural network using triangle kernel function (QRNNT) is proposed. The nonlinear structure of neural network is applied to transform the quantile regression model for constructing probabilistic forecasting method. Moreover, the triangle kernel function and direct plug-in bandwidth selection method are employed to perform kernel density estimation. To verify the efficiency, the proposed method is used for Canada's and China's load forecasting. The complete probability density curves are obtained to indicate the QRNNT method is capable of forecasting high quality prediction interval (PIs) with higher coverage probability. Numerical results also confirm favorable performance of proposed method in comparison with the several existing forecasting methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Energy - Volume 114, 1 November 2016, Pages 498-512
نویسندگان
, , , ,