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Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest
المؤلفون المشاركون
Sun, Zexian
Zhang, Jingxuan
Sun, Hexu
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2018، العدد 2018 (31 ديسمبر/كانون الأول 2018)، ص ص. 1-15، 15ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2018-07-24
دولة النشر
مصر
عدد الصفحات
15
التخصصات الرئيسية
الملخص EN
A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF).
However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions.
Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy.
First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP).
Then, the WSP data are transformed into supplementary input features in the prediction process of WPF.
Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model.
In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively.
The increasing number of training samples would cause the overfitting problem.
Therefore, the k-fold cross validation (CV) technique is adopted to address this issue.
Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Sun, Zexian& Sun, Hexu& Zhang, Jingxuan. 2018. Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1208282
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Sun, Zexian…[et al.]. Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest. Mathematical Problems in Engineering No. 2018 (2018), pp.1-15.
https://search.emarefa.net/detail/BIM-1208282
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Sun, Zexian& Sun, Hexu& Zhang, Jingxuan. Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-15.
https://search.emarefa.net/detail/BIM-1208282
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1208282
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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