基于MA-CNN-LSTM和自注意力机制的单变量短期电力负荷预测

Univariate Short-term Electrical Load Based on MA-CNN-LSTM-Self Attention

精准的短期电力负荷预测对保证电网安全稳定运行、能量优化管理、提高发电设备利用率和降低运行成本等具有重要作用。针对单变量场景下地区短期电力负荷预测问题,提出了一种基于多重滑动平均(moving average,MA)和卷积网络-长短期记忆网络(convolutional networks long short-term memory networks,CNN-LSTM)混合模型,并添加自注意力(Self-Attention)机制的预测方法。首先利用多重滑动平均将原始负荷数据分解为多个平稳序列,以降低数据的噪声和复杂度。接着将各一维序列数据变换为多维结构,使用CNN提取多个时间点之间的内在关系。再输入LSTM模型中训练,并使用自注意力机制进行加权融合以提高预测精度。最后把各序列预测值相加得到最终负荷预测值。为了验证该方法的有效性,在中国某地区电网间隔15 min的真实负荷数据上进行了预测实验,并将预测结果与其他常见的模型预测结果进行对比。通过实验结果表明,在单变量短期电力负荷预测问题中该方法的准确性比其他方法更高。

Accurate short-term power load forecasting plays an important role in ensuring the safe and stable operation of the power grid,optimizing energy management,improving the utilization rate of power generation equipment and reducing operating costs.Aiming at the problem of regional short-term power load prediction in univariate scenarios,a prediction method based on MA(moving average)and CNN-LSTM(convolutional networks long short-term memory networks)was proposed,and self-attention mechanism was added.Firstly,multiple sliding average was used to decompose the raw load data into multiple stationary series to reduce the noise and com-plexity of the data.Secondly,the one-dimensional sequence data was transformed into a multidimensional structure,and the CNN was used to extract the internal relationship between multiple time points.Then,data was input into the LSTM model for training,and weighted fusion was performed using the Self-Attention mechanism to improve the prediction accuracy.Finally,the predicted values of each series are added together to obtain the final load prediction value.In order to verify the effectiveness of the method,a prediction experiment was carried out on the real load data of the power grid interval of fifteen minutes in a certain region of China,and the pre-diction results were compared with other common model prediction results.Experimental results show that the accuracy of this method is higher than that of other methods in univariate short-term power load forecasting problems.