论文标题
基于双向扩张的残留网络进行点学习的顺序，用于非侵入载荷监测
Sequence to Point Learning Based on Bidirectional Dilated Residual Network for Non Intrusive Load Monitoring
论文作者
论文摘要
非侵入性负载监测（NILM）或能量分解（ED）试图通过分解整个房屋的总功率读取相应的电器功率来节省能量。这是一个单个通道盲源分离问题（SCBS）和困难的预测问题，因为它是无法识别的。最近的研究表明，深度学习已成为尼尔姆问题的日益普及。神经网络提取负载特征的能力与其深度密切相关。但是，由于梯度爆炸，消失的梯度和网络降解，深度神经网络很难训练。为了解决这些问题，我们提出了一个序列，以基于尼尔姆的双向（非休闲）扩张卷积指向学习框架。更令人信服的是，我们将我们的方法与最先进的方法（Zhang）直接比较，并通过两个相同的数据集和指标间接地与现有算法进行比较。基于REDD和英国数据集的实验表明，我们提出的方法远远超过了所有设备中现有方法。
Non Intrusive Load Monitoring (NILM) or Energy Disaggregation (ED), seeks to save energy by decomposing corresponding appliances power reading from an aggregate power reading of the whole house. It is a single channel blind source separation problem (SCBSS) and difficult prediction problem because it is unidentifiable. Recent research shows that deep learning has become a growing popularity for NILM problem. The ability of neural networks to extract load features is closely related to its depth. However, deep neural network is difficult to train because of exploding gradient, vanishing gradient and network degradation. To solve these problems, we propose a sequence to point learning framework based on bidirectional (non-casual) dilated convolution for NILM. To be more convincing, we compare our method with the state of art method, Seq2point (Zhang) directly and compare with existing algorithms indirectly via two same datasets and metrics. Experiments based on REDD and UK-DALE data sets show that our proposed approach is far superior to existing approaches in all appliances.
