Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach
作者: Qiaomu ZhuJinfu ChenLin ZhuXianzhong DuanYilu Liu
作者单位: 1tate Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
2State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Hubei Electric Power Security and High Efficiency Key laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074,
刊名: Energies, 2018, Vol.11 (4)
来源数据库: Directory of Open Access Journals
DOI: 10.3390/en11040705
关键词: Convolutional neural networksDeep learningMachine learningSpatio-temporal correlationWind speed prediction
原始语种摘要: Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically....
全文获取路径: DOAJ  (合作)
分享到:

×
关键词翻译
关键词翻译
  • temporal 现世的
  • perceptron 感知器感知机
  • convolutional 卷积
  • prediction 预报
  • features 特征
  • regressor 回归量
  • learning 学识
  • spatial 空间的
  • neural 神经系统的
  • speed 速率