PHOTOVOLTAIC POWER PREDICTION BASED ON TCN-BILSTM-ATTENTION-ESN
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摘要: 针对光伏发电功率随机性强、难以准确预测的问题，提出一种基于时间卷积网络（TCN）、双向长短期记忆网络（BiLSTM）和回声状态网络（ESN）的组合预测方法。首先，使用自适应噪声完备集合经验模态分解（CEEMDAN）将功率数据分解为一系列相对平稳、不同波动模式的子功率序列；再将分解重构后的功率序列和其他特征序列输入到TCN-BiLSTMAttention-ESN组合模型中，其中TCN-BiLSTM-Attention用于提取光伏序列波动特征并构建时空特征向量；最后，将所提取的时空特征向量输入ESN获得预测结果。采用新疆某光伏电站的光伏功率数据进行验证，结果表明与时下先进的预测方法相比，所提方法具有更高的预测精度，有助于提升光伏发电占比，保障电力系统平衡和运行安全。Abstract: Aiming at the problem of strong randomness of photovoltaic power data and difficulty in accurate prediction, a combined prediction method based on temporal convolutional network（TCN）, bidirectional long short-term memory network（BiLSTM） and echo state network（ESN） is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise analysis（CEEMDAN） is used to decompose the power data into a series of relatively stable sub power subsequences. Then, the decomposed and reconstructed power sequence and other feature sequences are input into the TCN-BiLSTM Attention-ESN. TCN-BiLSTM Attention-ESN is applied to extract features and then spatiotemporal feature vectors are constructed. Finally, the extracted spatiotemporal feature vectors are input into ESN to obtain the prediction results. The proposed method is validated using photovoltaic power data from photovoltaic power stations in Xinjiang, China. The results showe that compared with current advanced prediction methods, the proposed method has higher prediction accuracy, which helps to increase the proportion of photovoltaic power generation and ensure the balance and operation safety of the power system.
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