کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6764240 | 1431578 | 2018 | 22 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine
ترجمه فارسی عنوان
یک رویکرد پیشرفته برای فواصل پیش بینی تولید انرژی باد با استفاده از دستگاه یادگیری افراطی تکاملی خود سازگار
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کلمات کلیدی
شبکه های عصبی مصنوعی، نمونه برداری بوت استرپ، تکامل دیفرانسیل، دستگاه یادگیری افراطی تکاملی خود سازگار، ماشین بردار پشتیبانی، فواصل پیش بینی تولید انرژی باد،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
This paper proposes a novel and hybrid intelligent algorithms to directly modelling prediction intervals (PIs), as an accurate, optimum, reliable and high efficient wind power generation prediction intervals (PIs) are developed by using extreme learning machines (ELM) and self-adaptive evolutionary extreme learning machines (SAEELM). Given significant of uncertainties existed in the wind power generation, SAEELM is the state-of-the-art technology to estimate and quantify the potential uncertainties that may result in risk facing the power system planning, economical operation, and control. In SAEELM, a single hidden layer extreme learning machine is constructed, where the output weight matrix is optimised by using the self-adaptive differential evolution (DE) optimisation method. Also, selecting and adjusting the control parameters and generation strategies involved in differential evolution algorithm to minimises the developed objective cost function. Different case studies using Australian real wind farms have been conducted and analysed. By comparing the statistical analysis and results to other models and methods, e.g. artificial neural networks (ANN), support vector machines (SVM), and Bootstrap, therefore, the proposed approach is an efficient, accurate, robust, and reliable for dealing with uncertainties involved in the integrated power systems, and generation of high-quality PIs. Moreover, the proposed SAEELM based algorithm has a better generalisation than other methods and has a high potential for practical applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 126, October 2018, Pages 254-269
Journal: Renewable Energy - Volume 126, October 2018, Pages 254-269
نویسندگان
Tawfek Mahmoud, Z.Y. Dong, Jin Ma,