RESEARCH ON WIND SPEED INTERVAL PREDICTION BASED ON HYBRID DEEP LEARNING MODEL

被引：0
|
作者：
Ma C. [1 ]
Wang C. [1 ]
Wang X. [2 ]
Zhang H. [1 ]
机构：
[1] College of Electrical Engineering, Xinjiang University, Urumqi
[2] School of Electrical Engineering, Xinjiang University, Urumqi
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 03期
关键词：
attention mechanism; Gaussian process regression; long short-term memorynetwork; wind power; wind speed prediction;
D O I：
10.19912/j.0254-0096.tynxb.2021-1241
中图分类号：
学科分类号：
摘要：
The uncertainty of wind speed makes it more difficult to predict wind speed，and wind energy is difficult to be used effectively. In order to solve the above problems，a hybrid depth learning model for wind speed interval prediction is proposed based on Convolutional Neural Network（CNN），Shared Weight Long Short-Term Memory Network（SWLSTM），Attention Mechanism（AM）and Gaussian Process Regression（GPR）. Firstly，the network combined CNN and SWLSTM is used to extract the features of wind speed series. Secondly，AM module is added to make use of the feature vector. Finally，the interval prediction is carried out through GPR. The model is applied to two wind speed data sets to test，and compared with other wind speed prediction models from two aspects of point prediction accuracy and interval prediction results. The experimental results show that the prediction model can obtain high-precision prediction results and appropriate prediction interval. © 2023 Science Press. All rights reserved.
引用
收藏
页码：139 / 146
页数：7
相关论文
共 17 条
  • [1] DONG Y C, ZHANG H L, WANG C，, Et al., A novel hybrid model based on bernstein polynomial with mixture of Gaussians for wind power forecasting［J］, Applied energy, 286, (2021)
  • [2] WANG C, ZHANG H L，, MA P., Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network［J］, Applied energy, 259, (2020)
  • [3] LIANG C, LIU Y Q, ZHOU J K, Et al., Wind speed prediction at multi-locations based on combination of recurrent and convolutional neural networks［J］, Power system technology, 45, 2, pp. 534-542, (2021)
  • [4] ZHU Q M, LI H Y, WANG Z Q，, Et al., Short- term windpower forecasting based on LSTM［J］, Power system technology, 41, 12, pp. 3792-3802, (2017)
  • [5] WEI Y Z，, XU X N., Ultra-short term wind speedprediction based on LSTM long and short term memorynetwork［J］, Journal of electronic measurement and instrumentation, 33, 2, pp. 64-71, (2019)
  • [6] LI Z Y，, AI Q, ZHANG Y F，, Et al., A LSTM neuralnetwork method based on attention mechanism for ultra short- term load forecasting［J］, Distribution & utilization, 36, 1, pp. 17-22, (2019)
  • [7] LIU B L, ZHANG H L, WANG C, Et al., Ultra-short-term wind speed predition based on sequence-to-sequence and attention mechanism［J］, Acta energiae solaris sinica, 42, 9, pp. 286-294, (2021)
  • [8] ZHANG Z D, YE L，, QIN H，, Et al., Wind speed prediction method using shared weight long short-term memory network and gaussian process regression［J］, Applied energy, 247, pp. 270-284, (2019)
  • [9] YI X H，, LIU ZH, Et al., Application of novel hybrid deep leaning model for cleaner production in a paper industrial wastewater treatment system［J］, Journal of cleaner production, 294, (2021)
  • [10] Using SARIMA-CNN-LSTM approach to forecast daily tourism demand［J］, Journal of hospitality and tourism management, 49, pp. 25-33, (2021)
12