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A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information
المؤلفون المشاركون
Zhou, Baobin
Liu, Che
Li, Jianjing
Sun, Bo
Yang, Jun
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-09-28
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid.
Meteorological features and seasonal information are strongly related to wind power prediction.
This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information.
The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD).
The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity.
With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction.
Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM.
The proposed method is used to predict 15-minute wind power.
In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method.
The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Zhou, Baobin& Liu, Che& Li, Jianjing& Sun, Bo& Yang, Jun. 2020. A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193541
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Zhou, Baobin…[et al.]. A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information. Mathematical Problems in Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1193541
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Zhou, Baobin& Liu, Che& Li, Jianjing& Sun, Bo& Yang, Jun. A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1193541
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1193541
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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