基于改进子任务门控网络的非侵入居民负荷分解

Non-intrusive residential load disaggregation based on advanced SGN

非侵入负荷分解对挖掘用户侧能源需求具有重要意义。但是,目前功率分解模型收敛困难且推理周期计算量大。研究了基于序列到子序列和子任务门控网络(SGN)的非侵入负荷分解模型。首先,采用序列到子序列的方法构建子任务门控网络,将功率分解回归任务和电器状态分类任务相结合,实现主电源序列到目标电器子序列的映射;然后添加通道注意力模块和空间注意力模块提高模型的特征提取能力。基于UK-dale数据集的实验结果表明,该方法不仅减小了模型收敛的困难度和推理周期的计算量,而且显著提高了分解的精度。

Non-intrusive residential load disaggregation is of great significance for mining the energy demand on the user side.However,the current power decomposition model does not converge well and the inference cycle is computationally intensive.A non-intrusive load decomposition model based on sequence-to-subsequence and subtask gated networks(SGN)is investigated in the paper.Firstly,a sequence-to-subsequence approach is used to construct a sub-task gating network,combining a power decomposition regression task with an appliance state classification task to achieve a mapping from the main power sequence to the target appliance sub-series.Then a channel attention module and a spatial attention module are added to improve the feature extraction capability of the model.Experimental results based on the UK-dale dataset show that the method not only reduces the difficulty of convergence of the model and the computational effort of the inference cycle,but also significantly improves the accuracy of the decomposition.