中圖分類號： TP181 文獻(xiàn)標(biāo)識碼： A DOI：10.16157/j.issn.0258-7998.212141 中文引用格式： 許皓宇,，薛巍,，張濤，等. 基于空間深度置信網(wǎng)絡(luò)的風(fēng)速預(yù)測優(yōu)化方法[J].電子技術(shù)應(yīng)用,，2022,，48(8)：111-116，122. 英文引用格式： Xu Haoyu,，Xue Wei,，Zhang Tao，et al. The improvement of wind speed prediction using spatial deep belief network[J]. Application of Electronic Technique,，2022,，48(8)：111-116，122.
The improvement of wind speed prediction using spatial deep belief network
Xu Haoyu1,，Xue Wei1,，Zhang Tao1，Xie Hongliang2
1.Department of Computer Science and Technology,，Tsinghua University,，Beijing 100084，China,； 2.Envision Energy Software Technology Limited,，Shanghai 200050，China
Abstract： Wind energy is the most widely used renewable energy. Accurate wind speed prediction is critical for the safety and stability of wind power system. Besides traditional numerical weather prediction, the machine learning technique has been used in wind speed prediction of different time scales. However, most previous studies focused on the wind speed sequence of single station and ignored the spatial dependency and correlation of wind. To improve the prediction with spatial information, this paper tries to extract the wind spatial correlation features in one region area and reconstruct the wind speed using deep belief network(DBN). The experiment results of different regions prove that the spatial deep belief network can reduce the prediction error significantly and increase the accuracy of wind speed prediction by 0.4 m/s on average.
Key words : deep belief network,；wind speed prediction,；Gaussian process regression
