Stephen Makonin

Richard Jones, Christoph Klemenjak, Stephen Makonin et al.

Non-Intrusive Load Monitoring (NILM) is a field of research focused on segregating constituent electrical loads in a system based only on their aggregated signal. Significant computational resources and research time are spent training models, often using as much data as possible, perhaps driven by the preconception that more data equates to more accurate models and better performing algorithms. When has enough prior training been done? When has a NILM algorithm encountered new, unseen data? This work applies the notion of Bayesian surprise to answer these questions which are important for both supervised and unsupervised algorithms. We quantify the degree of surprise between the predictive distribution (termed postdictive surprise), as well as the transitional probabilities (termed transitional surprise), before and after a window of observations. We compare the performance of several benchmark NILM algorithms supported by NILMTK, in order to establish a useful threshold on the two combined measures of surprise. We validate the use of transitional surprise by exploring the performance of a popular Hidden Markov Model as a function of surprise threshold. Finally, we explore the use of a surprise threshold as a regularization technique to avoid overfitting in cross-dataset performance. Although the generality of the specific surprise threshold discussed herein may be suspect without further testing, this work provides clear evidence that a point of diminishing returns of model performance with respect to dataset size exists. This has implications for future model development, dataset acquisition, as well as aiding in model flexibility during deployment.

Alon Harell, Richard Jones, Stephen Makonin et al.

Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most current techniques for NILM are trained using significant amounts of labeled appliances power data. The collection of such data is challenging, making data a major bottleneck in creating well generalizing NILM solutions. To help mitigate the data limitations, we present the first truly synthetic appliance power signature generator. Our solution, PowerGAN, is based on conditional, progressively growing, 1-D Wasserstein generative adversarial network (GAN). Using PowerGAN, we are able to synthesise truly random and realistic appliance power data signatures. We evaluate the samples generated by PowerGAN in a qualitative way as well as numerically by using traditional GAN evaluation methods such as the Inception score.

Christoph Klemenjak, Stephen Makonin, Wilfried Elmenreich

Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techniques, performance evaluation and comparability remains an open research question. The lack of standardisation and consensus on evaluation procedures makes reproducibility and comparability extremely difficult. In this paper, we draw attention to comparability in NILM with a focus on highlighting the considerable differences amongst common energy datasets used to test the performance of algorithms. We divide discussion on comparability into data aspects, performance metrics, and give a close view on evaluation processes. Detailed information on pre-processing as well as data cleaning methods, the importance of unified performance reporting, and the need for complexity measures in load disaggregation are found to be the most urgent issues in NILM-related research. In addition, our evaluation suggests that datasets should be chosen carefully. We conclude by formulating suggestions for future work to enhance comparability.

Christoph Klemenjak, Anthony Faustine, Stephen Makonin et al.

To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cross-validation by evaluating the classification and energy estimation performance on unseen portions of the dataset derived from the same households. With an emerging discussion of transferability in Non-Intrusive Load Monitoring (NILM), there is a need for domain-specific metrics to assess the performance of NILM algorithms on new test scenarios being unseen buildings. In this paper, we discuss several metrics to assess the generalisation ability of NILM algorithms. These metrics target different aspects of performance evaluation in NILM and are meant to complement the traditional performance evaluation approach. We demonstrate how our metrics can be utilised to evaluate NILM algorithms by means of two case studies. We conduct our studies on several energy consumption datasets and take into consideration five state-of-the-art as well as four baseline NILM solutions. Finally, we formulate research challenges for future work.

Laura Guzman, Stephen Makonin, Roger Alex Clapp

Ubiquitous technology platforms have been created to track and improve health and fitness; similar technologies can help individuals monitor and reduce their carbon footprints. This paper proposes CarbonKit, a platform combining technology, markets, and incentives to empower and reward people for reducing their carbon footprint. We argue that a goal-and-reward behavioral feedback loop can be combined with the Big Data available from tracked activities, apps, and social media to make CarbonKit an integral part of individuals daily lives. CarbonKit comprises five modules that link personal carbon tracking, health and fitness, social media, and economic incentives. Protocols for safeguarding security, privacy and individuals control over their own data are essential to the design of the CarbonKit. Initially CarbonKit would operate on a voluntary basis, but such a system can also serve as part of a mandatory region-wide initiative. We use the example of the British Columbia to illustrate the regulatory framework and participating stakeholders that would be required to support the CarbonKit in specific jurisdictions.

Md. Zulfiquar Ali Bhotto, Stephen Makonin, Ivan V. Bajic

Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence does not crucially depend on high sampling frequency. Experimental results show that the proposed ALIP system performs better than the conventional IP-based load disaggregation system.

Stephen Makonin, Maryam H. Kashani, Lyn Bartram

As people become more concerned with the need to conserve their power consumption we need to find ways to inform them of how electricity is being consumed within the home. There are a number of devices that have been designed using different forms, sizes, and technologies. We are interested in large ambient displays that can be read at a glance and from a distance as informative art. However, from these objectives come a number of questions that need to be explored and answered. To what degree might lifestyle factors influence the design of eco-visualizations? To answer this we need to ask how people with varying lifestyle factors perceive the utility of such devices and their placement within a home. We explore these questions by creating four ambient display prototypes. We take our prototypes and subject them to a user study to gain insight as to the questions posed above. This paper discusses our prototypes in detail and the results and findings of our user study.