کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4926823 | 1363187 | 2017 | 32 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A novel forecasting model based on a hybrid processing strategy and an optimized local linear fuzzy neural network to make wind power forecasting: A case study of wind farms in China
ترجمه فارسی عنوان
یک مدل پیش بینی جدید بر مبنای یک استراتژی پردازش ترکیبی و یک شبکه عصبی فازی خطی محلی به منظور پیش بینی پیش بینی توان باد: مطالعه موردی مزارع باد در چین
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کلمات کلیدی
قدرت باد، استراتژی پردازش، شبکه عصبی فازی خطی محلی، پیش بینی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی انرژی
انرژی های تجدید پذیر، توسعه پایدار و محیط زیست
چکیده انگلیسی
As a crucial issue in the wind power industry, it is a tough and challenging task to predict the wind power accurately because of its nonlinearity, non-stationary and chaos. In this paper, we propose a novel hybrid model, which combines an integrated processing strategy and an optimized local linear fuzzy neural network, to forecast the wind power. First, discrete wavelet transform and singular spectrum analysis are used to filter out the noises and extract the trends from original wind power series, respectively. Then, the novel no-negative-constraint-combination theory together with the CS algorithm are adopted to integrate these two subseries obtained from the first step to retain strengths of each method. Based on the phase space reconstruction model, we could determine the most proper structure of the input sets and the output sets. Next, the local linear fuzzy neural network, with the initial rule consequent parameters optimized by the seeker optimization algorithm, is utilized to make wind power forecasts for a selected number of forward time steps. The numerical results from two experiments demonstrate that the proposed hybrid model is an effective approach to predict wind power, and the accuracy of prediction is highly improved compared with conventional forecasting models.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Renewable Energy - Volume 102, Part A, March 2017, Pages 241-257
Journal: Renewable Energy - Volume 102, Part A, March 2017, Pages 241-257
نویسندگان
Qingli Dong, Yuhuan Sun, Peizhi Li,