基于改进深度森林的短期电力负荷预测模型

深度学习模型通过学习数据的深层特征能够有效提高电力负荷预测的准确率，但同时也带来了超参数较多、模型可解释性差等问题。针对这些问题，文中将深度森林模型引入短期电力负荷预测领域。在多粒度级联森林模型的基础上改进了多粒度窗口扫描方法，调整窗口大小与滑动步长，使模型能够在不同时间尺度下提取电力负荷数据的周期性特征。此外，改进深度森林输出层的计算方法，将输出结果由离散的类向量改进为连续的预测值，进而提高模型的精确度。最后在中国东北电网的实测数据中验证了文中所提出方法的可行性与有效性。从实验结果可知，改进深度森林算法在较高预测精度的情况下能取得更高的准确率，并且相较于深度神经网络具有更快的学习速度。

Short term power load forecasting model based on improved deep forest

Deep learning method can help to learn the deep features of power load data and improve the accuracy of prediction, but it also brings problems, such as large amounts of super parameters and poor interpretability of the model. To solve these problems, this paper introduces the deep forest model for short-term load forecasting. Based on the multi-Grained Cascade forest model, the multi-granularity window scanning method is improved with adjusted window size and sliding step size, so that the model can extract the periodicity characteristics of power load data in different time scales. In addition, the calculation method of deep forest output layer is improved with changing the output result from discrete class vector to continuous predicted value, improving the accuracy of the model. Finally, the feasibility and effectiveness of the proposed method are verified with the measured data of northeast China power grid. The experimental results show that the improved deep forest algorithm can achieve higher accuracy with higher prediction accuracy, and has faster learning speed than the deep neural network.