Non-Intrusive Load Monitoring (NILM) is an energy efficiency technique to
track electricity consumption of an individual appliance in a household by one
aggregated single, such as building level meter readings. The goal of NILM is
to disaggregate the appliance from the aggregated singles by computational
method. In this work, deep learning approaches are implemented to operate the
desegregations. Deep neural networks, convolutional neural networks, and
recurrent neural networks are employed for this operation. Additionally, sparse
evolutionary training is applied to accelerate training efficiency of each deep
learning model. UK-Dale dataset is used for this work.

本研究使用深度学习方法来对非侵入式负载监测（NILM）进行数据脱密，使用神经网络和 UK-Dale 数据集来加速学习效率和分析能耗。

用于非侵入式负载监测的进化深度网络

Evolutionary Deep Nets for Non-Intrusive Load Monitoring

Many countries are rolling out smart electricity meters. These measure a
home's total power demand. However, research into consumer behaviour suggests
that consumers are best able to improve their energy efficiency when provided
with itemised, appliance-by-appliance consumption information. Energy
disaggregation is a computational technique for estimating
appliance-by-appliance energy consumption from a whole-house meter signal.
To conduct research on disaggregation algorithms, researchers require data
describing not just the aggregate demand per building but also the `ground
truth' demand of individual appliances. In this context, we present UK-DALE: an
open-access dataset from the UK recording Domestic Appliance-Level Electricity
at a sample rate of 16 kHz for the whole-house and at 1/6 Hz for individual
appliances. This is the first open access UK dataset at this temporal
resolution. We recorded from five houses, one of which was recorded for 655
days, the longest duration we are aware of for any energy dataset at this
sample rate. We also describe the low-cost, open-source, wireless system we
built for collecting our dataset.

本篇研究介绍了一种低成本的无线系统，并利用已收集的数据集 (UK-DALE) 提出了一种能够通过整个家庭的电表信号来估算每个电器用电量的计算技术 - 能源分解技术，并发现消费者通过个性化的电器用电量信息最有效地提高他们的能源效率。