基于深度学习的电网短期负荷预测方法研究

深度模型通过学习一种深层非线性网络结构以实现复杂函数逼近,具有很强的自适应感知能力。本文为了提高电力负荷预测精度,提出一种基于栈式自编码神经网络的深度学习预测方法。该方法结合自编码器和逻辑回归分类器构建一个多输入单输出预测模型,并将重构后的历史负荷、气象信息等数据输入到预测模型中,用栈式自编码器逐层学习并提取深层特征,最后在网络顶层连接逻辑回归模型进行短期负荷预测。实例分析表明,所提预测模型能够有效刻画日负荷变化规律,泛化能力较强,其预测精度达到96.2%,比支持向量回归和模糊神经网络两种浅层学习模型更高。

Research on Short-term Load Forecasting Method of Power Grid Based on Deep Learning

The depth model achieves complex function approximation by learning a deep nonlinear network structure,which has strong adaptive perception ability. In order to improve the prediction accuracy of power load, a deep learning prediction method based on stacked auto-encoder neural network is proposed in the paper. A multi-input single-output prediction model is built by combing the auto-encoder with the logic regression classifier, such data as the reconstructed historical load, meteorological elements and so on are all input into prediction model, and the load characteristics is extracted through the hierarchical learning of the stacked auto-encoder. Finally, the short-term load prediction is realized by using the logical regression model at the top of the network. Case analysis shows that the proposed model can effectively characterize the daily load change law with strong generalization performance, and its prediction accuracy can reach 96.2%, which is higher than that of two shallow learning models based on support vector regression and fuzzy neural network respectively.