光伏短期发电数据维数高,特征复杂,数据特征的分解提取和预测模型的构建是影响预测效果的关键,该文提出一种结合增量学习的嵌入元启发大猩猩参数优化的光伏发电短期预测方法GVMD-TSNE-TCN-LSTMre,第一层的特征提取采用变分模态分解（VMD）和T分布随机近邻嵌入（TSNE）模型,二者结合获得光伏数据中的有效特征,其中VMD 涉及惩罚因子和分解模态数两个关键参数的选择,采用元启发大猩猩优化算法（GTO）对其参数进行优化,获得优化特征提取方法（GVMD）;第二层的预测模型构建,结合时序卷积神经网络（TCN）和长短期记忆网络（LSTM）建立TCN-LSTM预测模型,完成各特征的学习、叠加和重构,在此基础上采用增量学习的方法（GVMD-TSNE-TCN-LSTMre）,基于参数冻结和全链接层更新的增量设计方法不断修改预测模型。最后,采用甘肃省某光伏场功率数据进行仿真验证,验证GVMD-TNSE数据处理的必要性、GTO参数优化算法对所选模型的时效性,以及整体模型的有效性。
Abstract
The data of PV short-term power generation has high dimension and complex features. The key factors affecting the forecasting performance include the decomposition , extraction of data features and the construction of prediction model. In this paper, an improved PV short-term prediction method embedded gorilla parameter optimization algorithm is proposed. In the first layer, variational mode decomposition （VMD） and T-distributed stochastic neighbor（TSNE） embedding are adopted for the feature extraction, which are combined to obtain effective features of the photovoltaic data. VMD involves the selection of two key parameters, namely penalty factor and decomposition mode number. In this paper, the gorilla optimization algorithm is used to optimize these parameters, denoted as GVMD.The second layer is to construct the prediction model. The TCN-LSTM prediction model is built by combining the temporal convolutional neural network （TCN） and the long short-term memory network （LSTM）, and the learning, superposition and reconstruction of various features are completed.On this basis, the incremental learning method, denoted as GVMD-TSNE-TCN-LSTMre is used to continuously modify the prediction model by the design of parameter freezing and full-connection layer update. Finally, the power data of a photovoltaic field in Gansu Province was simulated to verify the necessity of GVMD-TNSE data processing, the timeliness of GTO parameter optimization algorithm, and the effectiveness of the overall model.
关键词
光伏发电 /
短期功率预测 /
增量学习 /
大猩猩优化算法 /
GVMD-TSNE特征分解提取 /
TCN-LSTM预测模型
Key words
photovoltaic power generation /
short-term power prediction /
incremental learning method /
gorilla troops optimizer /
GVMD-TSNE feature decomposition extraction /
TCN-LSTM prediction model
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基金
南方电网科技项目（YNKJXM20222151）; 国家自然科学基金（52269020）
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