کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7408237 | 1481436 | 2016 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
موضوعات مرتبط
علوم انسانی و اجتماعی
مدیریت، کسب و کار و حسابداری
کسب و کار و مدیریت بین المللی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting A hybrid model of kernel density estimation and quantile regression for GEFCom2014 probabilistic load forecasting](/preview/png/7408237.png)
چکیده انگلیسی
We present a model for generating probabilistic forecasts that combines the kernel density estimation (KDE) and quantile regression techniques, as part of the probabilistic load forecasting track of the Global Energy Forecasting Competition 2014. Initially, the KDE method is implemented with a time-decay parameter, but we later improve this method by conditioning on the temperature or period of the week variables in order to provide more accurate forecasts. Secondly, we develop a simple but effective quantile regression forecast. The novel aspects of our methodology are two-fold. First, we introduce symmetry into the time-decay parameter of the kernel density estimation based forecast. Second, we combine three probabilistic forecasts with different weights for different periods of the month.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 32, Issue 3, JulyâSeptember 2016, Pages 1017-1022
Journal: International Journal of Forecasting - Volume 32, Issue 3, JulyâSeptember 2016, Pages 1017-1022
نویسندگان
Stephen Haben, Georgios Giasemidis,