End-to-end NILM System Using High Frequency Data and Neural Networks
Improving energy efficiency is a necessity in the fight against climate change. Non Intrusive Load Monitoring (NILM) systems give important information about the household consumption that can be used by the electric utility or the end users. In this work the implementation of an end-to-end NILM system is presented, which comprises a custom high frequency meter and neural-network based algorithms. The present article presents a novel way to include high frequency information as input of neural network models by means of multivariate time series that include carefully selected features. Furthermore, it provides a detailed assessment of the generalization error and shows that this class of models generalize well to new instances of seen-in-training appliances. An evaluation database formed of measurements in two Uruguayan homes is collected and discussion on general unsupervised approaches is provided.
Code (0)
등록된 구현이 없습니다.
Tasks
Non-Intrusive Load MonitoringTime SeriesTime Series AnalysisSimilar Papers 제목 키워드 기반
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks have become increasingly popular in attem…
Non-Intrusive Load MonitoringToward Explainable NILM: Real-Time Event-Based NILM Framework for High-Frequency Data
Non-Intrusive Load Monitoring (NILM) is an advanced, and cost-effective technique for monitoring appliance-level energy consumption. However, its adaptability is hindered by the lack of transparency and explainability. T…
Non-Intrusive Load MonitoringData augmentation for dealing with low sampling rates in NILM
Data have an important role in evaluating the performance of NILM algorithms. The best performance of NILM algorithms is achieved with high-quality evaluation data. However, many existing real-world data sets come with a…
Data AugmentationHiFAKES: High-frequency synthetic appliance signatures generator for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) rely on data-driven methods and require extensive datasets of power consumption over a long period of time. However, the scarcity of datasets negatively impacts the performance of cur…
DiversityNon-Intrusive Load MonitoringCOLD: Concurrent Loads Disaggregator for Non-Intrusive Load Monitoring
The global effort toward renewable energy and the electrification of energy-intensive sectors have significantly increased the demand for electricity, making energy efficiency a critical focus. Non-intrusive load monitor…
Non-Intrusive Load Monitoring