风电预测
聚类分析
风力发电
概率预测
计算机科学
数值天气预报
可再生能源
功率(物理)
维数(图论)
数据挖掘
电力系统
期限(时间)
气象学
算法
人工智能
工程类
数学
地理
电气工程
物理
量子力学
概率逻辑
纯数学
作者
Yu Zhang,Yanting Li,Guangyao Zhang
出处
期刊:Energy
[Elsevier]
日期:2020-12-01
卷期号:213: 118371-118371
被引量:130
标识
DOI:10.1016/j.energy.2020.118371
摘要
Wind power is one of the main sources of renewable energy. Precise forecast of the power output of wind farms could greatly decrease the negative impact of wind power on power grid operation and reduce the cost of the power system operation. In this paper, a wind power output forecast model was proposed by integrating multivariate times series clustering algorithm with deep learning network. The NWP data and actual wind farm historical data were used as the input of the proposed model. 78 typical characteristic and statistical features were extracted from the inputs. Dimension reduction algorithm t-SNE was used to project the feature vectors into lower dimension and K-means algorithm was used to cluster the inputs into different clusters afterwards. At last, Seq2Seq with attention models were built for each cluster for power output prediction. The forecasting horizon is 1 day and the data resolution is 10 min. The results showed that the Seq2Seq model outperformed other existing forecasting methods such as Deep Belief Network and Random Forest. Clustering the input data into different clusters indeed improved the forecasting accuracy.
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