In this paper, a novel neural network architecture is proposed to address the challenges in energy disaggregation algorithms. These challenges include the limited availability of data and the complexity of disaggregating a large number of appliances operating simultaneously. The proposed model utilizes independent component analysis as the backbone of the neural network and is evaluated using the F1-score for varying numbers of appliances working concurrently. Our results demonstrate that the model is less prone to overfitting, exhibits low complexity, and effectively decomposes signals with many individual components. Furthermore, we show that the proposed model outperforms existing algorithms when applied to real-world data.

翻译：本文提出了一种新颖的神经网络架构，以应对能量分解算法中存在的挑战。这些挑战包括数据可用性有限以及同时运行大量电器时分解过程的复杂性。所提出的模型以独立成分分析作为神经网络的主干架构，并通过不同数量电器同时运行场景下的F1分数进行评估。实验结果表明，该模型具有较低的过拟合倾向，结构复杂度低，并能有效分解包含多个独立分量的信号。此外，我们证明该模型在应用于实际数据时性能优于现有算法。