Adaptive Weighted Recurrence Graph blocks for event-based NILM

To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task.

This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into a weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters.

We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG’s have a significant impact on the performance and training convergence.

We provide the implementation of AWRG on Github.  If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:

A. Faustine, L. Pereira and C. Klemenjak, "Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring," in IEEE Transactions on Smart Grid, doi: 10.1109/TSG.2020.3010621.

Learn more about the authors Anthony Faustine, Lucas Pereira and Christoph Klemenjak.

Supporting the Grid with Dynamic Residential Load Scheduling

Demand response (DR) for smart grids intends to balance the required power demand with the available supply resources. This is especially important with an increased amount of renewable energy sources since for most of them the energy yield cannot be shifted in time. For example, photovoltaic systems will provide their peak power at noon while customers might want to use energy at different times.

Residential load scheduling systems provide a solution to this problem by incentivizing consumers to use energy at times where production is high while motivating lower energy consumption in times of peak power. The goals of such residential load scheduling systems are therefore manifold: to cut peak power, to follow supply and to reduce the overall energy cost for the customer.

In a study we investigated different dynamic residential load scheduling systems with respect to optimal scheduling of household appliances on the basis of an adaptive consumption level pricing scheme (ACLPS). The proposed load scheduling system encourages customers to manage their energy consumption within the allowable consumption allowance of the proposed DR pricing scheme to achieve lower energy bills.

Dynamic pricing scheme motivating customers
to avoid energy consumption in peak periods

Simulation results show that employing the proposed approach benefits the customers by reducing their energy bill and the utility companies by decreasing the peak load of the aggregated load demand. For a given case study, the proposed residential load scheduling system based on ACLPS allows customers to reduce their energy bills by up to 53% and to decrease the peak load by  35%.

The full results are available in the paper

H. T. Haider, O. H. See, and W. Elmenreich. Dynamic residential load scheduling based on adaptive consumption level pricing scheme. Electric Power Systems Research, 133:27–35, 2016.