基于气象因素充分挖掘的BiLSTM光伏发电短期功率预测
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长安大学研究生科研创新实践项目(300103703041)


BiLSTM Short-Term Forecasting Method for Photovoltaic Power Generation Based on Fully Exploiting Meteorological Factors
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    摘要:

    传统光伏发电功率预测存在因气象因素特征提取不综合不精确而导致预测精度不高的问题. 为了充分挖掘气象因素对光伏出力的影响, 并有效利用深度学习技术在非线性拟合方面的优势, 本文提出了一种基于气象因素充分挖掘的双向长短期记忆(Bi-directional Long Short Term Memory, BiLSTM)网络光伏发电短期功率预测方法. 在对原始数据进行异常值及标准化处理的基础上, 采用K近邻算法(K-Nearest Neighbor, KNN)在外界温度、湿度、压强等诸多气象因素中充分挖掘影响光伏出力的关键因素, 重构多元数据序列, 并在探索输入层时间步长、模型层数及每层维数等超参数的合理设置方案的基础上, 构建BiLSTM网络模型, 实现光伏发电短期功率的高精度预测. 仿真结果表明, 与KNN、深度信念网络(DBN)、BiLSTM、PCA-LSTM等经典方法比较, 所提KNN-BiLSTM方法具有更高的预测精度.

    Abstract:

    The traditional PV power generation prediction has the problem that the prediction accuracy is not high due to incomplete and inaccurate feature extraction of meteorological factors. In order to fully explore the influence of meteorological factors on PV output and effectively utilize the advantage of deep learning technology in non-linear fitting, this study proposes a short-term forecasting method for PV power that is based on the full mining of meteorological factors and is realized through the BiLSTM network. Based on outliers and standardized processing of the original data, KNN is used to fully explore the key factors affecting PV output among meteorological factors such as external temperature, humidity, and pressure. And then multivariate data sequences are reconstructed. On the basis of exploring the reasonable setting scheme of the hyper parameters such as the time steps of the input layer, the number of model layers, and the dimensions of each layer, a BiLSTM network model is built to realize the high-precision prediction of short-term power of PV power generation. Simulation results show that the proposed KNN-BiLSTM method has higher prediction accuracy than the classical methods such as KNN, DBN, BiLSTM, and PCA-LSTM.

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徐先峰,刘阿慧,陈雨露,蔡路路.基于气象因素充分挖掘的BiLSTM光伏发电短期功率预测.计算机系统应用,2020,29(7):205-211

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  • 收稿日期:2019-12-24
  • 最后修改日期:2020-01-20
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  • 在线发布日期: 2020-07-04
  • 出版日期: 2020-07-15
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