window.dataLayer = window.dataLayer || []; function gtag(){dataLayer.push(arguments);} gtag('js', new Date()); gtag('config', 'UA-50665694-8');

Publication Types:

Sort by year:

Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks

Journal
Anthony Faustine, Lucas Pereira
Energies 2020, 13(13), 3374; doi: 10.3390/en13133374
Publication year: 2020

Abstract

Appliance recognition is one of the vital sub-tasks of NILM in which a machine learning classier is used to detect and recognize active appliances from power measurements. The performance of the appliance classifier highly depends on the signal features used to characterize the loads. Recently, different appliance features derived from the voltage–current (V–I) waveforms have been extensively used to describe appliances. However, the performance of V–I-based approaches is still unsatisfactory as it is still not distinctive enough to recognize devices that fall into the same category. Instead, we propose an appliance recognition method utilizing the recurrence graph (RG) technique and convolutional neural networks (CNNs). We introduce the weighted recurrent graph (WRG) generation that, given one-cycle current and voltage, produces an image-like representation with more values than the binary output created by RG. Experimental results on three different sub-metered datasets show that the proposed WRG-based image representation provides superior feature representation and, therefore, improves classification performance compared to V–I-based features.

Resources

Online Repository

PB-NILM: PinBall Guided Deep Non-Intrusive Load Monitoring

Journal
Eduardo Câmara, Lucas Pereira
IEEE Access, doi: 10.1109/ACCESS.2020.2978513
Publication year: 2020

Abstract

The work in this paper proposes the application of the pinball quantile loss function to guide a deep neural network for Non-Intrusive Load Monitoring. The proposed architecture leverages concepts such as Convolution Neural Networks and Recurrent Neural Networks. For evaluation purposes, this paper also presents a set of complementary performance metrics for energy estimation. Finally, this paper also reports on the results of a comprehensive benchmark between the proposed network and three alternative deep neural networks, when guided by the pinball and Mean Squared Error loss functions. The obtained results confirm the disaggregation superiority of the proposed system, while also showing that the performances obtained using the pinball loss function are consistently superior to the ones obtained using the Mean Squared Error loss.

Resources

Supplementary Material

Arbitrage with Power Factor Correction using Energy Storage

Journal
Md Umar Hashmi, Deepjyoti Deka, Ana Bušić, Lucas Pereira, Scott Backhaus
IEEE Transactions on Power Systems, doi: 10.1109/TPWRS.2020.2969978
Publication year: 2020

Abstract

The importance of reactive power compensation for power factor (PF) correction will significantly increase with the large-scale integration of distributed generation interfaced via inverters producing only active power. In this work, we focus on co-optimizing energy storage for performing energy arbitrage as well as local power factor correction. The joint optimization problem is non-convex, but can be solved efficiently using a McCormick relaxation along with penalty-based schemes. Using numerical simulations on real data and realistic storage profiles, we show that energy storage can correct PF locally without reducing arbitrage profit. It is observed that active and reactive power control is largely decoupled in nature for performing arbitrage and PF correction (PFC). Furthermore, we consider a real-time implementation of the problem with uncertain load, renewable and pricing profiles. We develop a model predictive control based storage control policy using auto-regressive forecast for the uncertainty. We observe that PFC is primarily governed by the size of the converter and therefore, look-ahead in time in the online setting does not affect PFC noticeably. However, arbitrage profit is more sensitive to uncertainty for batteries with faster ramp rates compared to slow ramping batteries.

Resources

Accepted Version

Understanding the practical issues of deploying energy monitoring and eco-feedback technology in the wild: Lesson learned from three long-term deployments

Journal
Lucas Pereira, Nuno Nunes
Energy Reports, vol. 6, pp. 94–106, Dec. 2019, doi: 10.1016/j.egyr.2019.11.025
Publication year: 2019

Abstract

This paper reports on the different engineering, social and financial challenges behind the building and deploying electric energy monitoring and eco-feedback technology in real-world scenarios, which despite being relevant to the research community are seldom reported in the literature. The objectives of this paper are two-fold: First, discuss the technical and social constraints of real-world deployments. This includes, for example, hardware and software requirements, and issues related to security and intrusiveness of the monitoring solutions. Second, identify and understand the costs associated with developing and deploying such systems. These include hardware costs and consumed energy. To this end, we rely on over five years of experience developing and improving a non-intrusive energy monitoring research platform to enable the deployment of long and short-term studies of eco-feedback technology. During this time, two versions of that platform were deployed in 50 homes for periods that lasted between 6 and 18 consecutive months. By iteratively developing and deploying our sensing and eco-feedback infrastructures, we managed to build upon previous findings and lessons learned to understand how to create, deploy, and maintain such systems. Concurrently, we gained insights regarding what are some of the most relevant costs associated with running such experiments.

Supplementary Material

NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring

Journal
Lucas Pereira
Data, vol. 4, no. 3, 127, Aug. 2019; doi: 10.3390/data4030127
Publication year: 2019

dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets

Journal
Manuel Pereira, Nuno Velosa, Lucas Pereira
Data, vol. 4, no. 3, 123, Aug. 2019; doi: 10.3390/data4030123
Publication year: 2019

A global monitoring system for electricity consumption and production of household roof-top PV systems in Madeira

Journal
Roham Torabi, Sandy Rodrigues, Nuno Cafôfo, Lucas Pereira, Filipe Quintal, Nuno Nunes, Fernando Morgado-Dias
Neural Computing and Applications, Oct. 2018; doi: 10.1007/s00521-018-3832-3
Publication year: 2018

Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory

Journal
Darío Baptista, Sheikh Shanawaz Mostafa, Lucas Pereira, Leonel Sousa, Fernando Morgado-Dias
Energies, vol. 11, no. 9, Sep. 2018; doi: 10.3390/en11092460
Publication year: 2018

Performance Evaluation in Non-Intrusive Load Monitoring: datasets, metrics and tools - a review

Journal
Lucas Pereira, Nuno Nunes
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Publication year: 2018

Understanding families’ motivations for sustainable behaviors

Journal
Mary L. Barreto, Agnieszka Szóstek, Evangelos Karapanos, Nuno J. Nunes, Lucas Pereira, Filipe Quintal
Computers in Human Behavior, Volume 40, Pages 6 - 15
Publication year: 2014

Show Me or Tell Me: Designing Avatars for Feedback

Journal
Michelle Scott, Lucas Pereira, Ian Oakley
Interacting with Computers. Volume 27, Issue 4, Pages 458-469
Publication year: 2014