کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
7408260 | 1481436 | 2016 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting
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کلمات کلیدی
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: K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting K-nearest neighbors and a kernel density estimator for GEFCom2014 probabilistic wind power forecasting](/preview/png/7408260.png)
چکیده انگلیسی
Probabilistic forecasts provide quantitative information in relation to energy uncertainty, which is essential for making better decisions on the operation of power systems with an increasing penetration of wind power. On the basis of the k-nearest neighbors algorithm and a kernel density estimator method, this paper presents a general framework for the probabilistic forecasting of renewable energy generation, especially for wind power generation. It is a direct and non-parametric approach. Firstly, the k-nearest neighbors algorithm is used to find the k closest historical examples with characteristics similar to the future weather condition of wind power generation. Secondly, a novel kernel density estimator based on a logarithmic transformation and a boundary kernel is used to construct wind power predictive density based on the k closest historical examples. The effectiveness of this approach has been confirmed on the real data provided for GEFCom2014. The evaluation results show that the proposed approach can provide good quality, reliable probabilistic wind power forecasts.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 32, Issue 3, JulyâSeptember 2016, Pages 1074-1080
Journal: International Journal of Forecasting - Volume 32, Issue 3, JulyâSeptember 2016, Pages 1074-1080
نویسندگان
Yao Zhang, Jianxue Wang,