Recently my work was published in conference like IFIP Interact, ICT4S, IFIP SustainIT, IEEE SmartGridComm, and ACM BuildSys. Below is an up-to-date listing of my publications. Check my Google Scholar and ORCID profiles for additional information.
Lucas Pereira
ITI, LARSyS, Técnico Lisboa
&
prsma.com
Publications
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Leveraging Machine Learning for Sustainable and Self-sufficient Energy Communities
Workshop
Tackling Climate Change with Machine Learning (NeurIPS 2020 Workshops)
Publication year: 2020
Economic Assessment of Solar-Powered Residential Battery Energy Storage Systems: the case of Madeira Island, Portugal
Abstract
This paper presents an economic assessment of introducing solar-powered residential battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against six local micro-producers using one year of energy consumption and solar photovoltaic production measurements and two distinct storage control strategies. Several inverter sizes and storage capacities were considered based on the six micro-producers’ consumption and production profiles. The results were then analyzed concerning year-long simulations and a projection for the next ten years. To this end, several indicators were assessed, including self-consumption, profit per Euro invested, number of cycles, and storage degradation. The results obtained show that despite the benefits of storage to increase the self-consumption rates, considerable drops in the storage prices are still necessary to achieve profitability during these devices’ lifetime. Furthermore, our results also highlight a very interesting trade-off between self-consumption, pre-charge, and profitability, in a sense that higher levels of pre-charge increase the chances of reaching profitability even though this will imply considerable drops in the levels of self-consumption.
UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM
Abstract
Over the years, an enormous amount of research has been exploring Deep Neural Networks (DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for estimating the energy consumption of appliances from a single point source such as smart meters – Non-Intrusive Load Monitoring (NILM).
However, most of the existing DNNs models for NILM use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. In this work, we propose UNet-NILM for multi-task appliances’ state detection and power estimation, applying a multi-label learning strategy and multi-target quantile regression. The UNet-NILM is a one-dimensional CNN based on the U-Net architecture initially proposed for image segmentation. Empirical evaluation on the UKDALE dataset suggests promising performance against traditional single-task learning.
On the Relationship between Seasons of the Year and Disaggregation Performance
Abstract
This paper pursues the question of how seasons of the year affect disaggregation performance in Non-Intrusive Load Monitoring.
To this end, we select the dishwasher, a common household appliance that may exhibit usage cycles depending on the user. We utilize an auto-correlation function to detect usage patterns of dishwashers in each season. Then, we examine the dissimilarity across each season with the help of the Keogh Lower Bound measure. Finally, we conduct a disaggregation study using the REFIT dataset and relate the outcome to the dissimilarity across seasons. Our findings indicate that in cases where energy consumption shows similarity throughout seasons, the performance of load disaggregation approaches can be positively affected.
An Empirical Exploration of Performance Metrics for Event Detection Algorithms in Non-Intrusive Load Monitoring
Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring
Multi-label Learning for Appliance Recognition in NILM using Fryze-Current Decomposition and Convolutional Neural Network
Abstract
The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.
Resources
Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks
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
Energy Storage Optimization for Grid Reliability
Abstract
Large scale renewable energy source (RES) integration planned for multiple power grids around the world will require additional resources/reserves to achieve secure and stable grid operations to mitigate the inherent intermittency of RES. In this paper, we present formulations to understand the effect of fast storage reserves in improving grid reliability under different cost functions. Our formulations not only aim to minimize imbalance but also maintain state-of-charge (SoC) of storage. The proposed approaches rely on a macroscopic supply-demand model of the grid with total power output of energy storage as the control variable. We show that accounting for system response due to inertia and local governor response enables a more realistic quantification of storage requirements for damping net load fluctuations. Simulation case studies are embedded in the paper by using datasets from the Elia TSO in Belgium and BPA in the USA. The numerical results benchmark the marginal effect on reliability due to increasing storage size under different system responses and associated cost functions. Further we observe myopic control of batteries proposed approximates deterministic control of batteries for faster time scale reserve operation.
Resources
Understanding the challenges behind Electric Vehicle usage by drivers - a case study in the Madeira Autonomous Region
Abstract
Electric Vehicles (EV) adoption targets have been set by governments from countries throughout Europe, related to the European goals, for the decarbonization of the road transport sector. The change for electric vehicle technology can be challenging to EV users for several reasons such as battery autonomy, time to charge the vehicle, and the different driving conditions. The work in this paper aims to study how users from Madeira and Porto Santo islands deal with the challenges of EV usage. Furthermore, this paper also studies the role of the orography in the Regenerative Braking System technology integrated into electric vehicles. To assess such information, an online questionnaire was prepared and sent out to the electric vehicle community of both islands.
The main results of this study show drivers’ preference to charge the vehicles at their household and that users are satisfied with the vehicle’s technology. Also, users’ battery range anxiety did not seem to have a significant impact. Moreover, from the drivers’ point of view, there is still the need to study the role of orography, while using the regenerative braking system.
Resources
PB-NILM: PinBall Guided Deep Non-Intrusive Load Monitoring
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
Sizing and Profitability of Energy Storage for Prosumers in Madeira, Portugal
Abstract
This paper proposes a framework to select the best-suited battery for co-optimizing for peak demand shaving, energy arbitrage and increase self-sufficiency in the context of power network in Madeira, Portugal. Feed-in-tariff for electricity network in Madeira is zero, which implies consumers with excess production should locally consume the excess generation rather than wasting it. Further, the power network {operator} applies a peak power contract for consumers which imposes an upper bound on the peak power seen by the power grid interfaced by energy meter. We investigate the value of storage in Madeira, using four different types of prosumers, categorized based on the relationship between their inelastic load and renewable generation. We observe that the marginal increase in the value of storage deteriorates with increase in size and ramping capabilities. We propose the use of profit per cycle per unit of battery capacity and expected payback period as indices for selecting the best-suited storage parameters to ensure profitability. This mechanism takes into account the consumption and generation patterns, profit, storage degradation, and cycle and calendar life of the battery. We also propose the inclusion of a friction coefficient in the original co-optimization formulation to increase the value of storage by reducing the operational cycles and eliminate low returning transactions.
Poster
Resources
Arbitrage with Power Factor Correction using Energy Storage
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
Understanding the practical issues of deploying energy monitoring and eco-feedback technology in the wild: Lesson learned from three long-term deployments
Journal
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
Co-optimizing Energy Storage for Prosumers Using Convex Relaxations
Abstract
This paper presents a new co-optimization formulation for energy storage for performing energy arbitrage and power factor correction (PFC) in the time scale of minutes to hours, along with peak demand shaving in the time scale of a month. While the optimization problem is non-convex, we present an efficient penalty based convex relaxation to solve it. Furthermore, we provide a mechanism to increase the storage operational life by tuning the cycles of operation using a friction coefficient. To demonstrate the effectiveness of energy storage performing multiple tasks simultaneously, we present a case study with real data for a time scale of several months. We are able to show that energy storage can realistically correct power factor without significant change in either arbitrage gains or peak demand charges. We demonstrate a real-time Model Predictive Control (MPC) based implementation of the proposed formulation with AutoRegressive forecasting of net-load and electricity price. Numerical results indicate that arbitrage gains and peak demand shaving are more sensitive to parameter uncertainty for faster ramping battery compared to slower ramping batteries. However, PFC gains are insensitive to forecast inaccuracies.
Ultrasonic Waste Monitoring in the Future Industrial Kitchen
Abstract
This paper presents the initial results of ultrasonic waste monitoring during the operation of one industrial kitchen. Undifferentiated, paper and plastic waste bins were monitored, and a heuristic-based waste disposal event detector was developed and evaluated. The results show that it is possible to identify disposal events in the three waste bins. Furthermore, it is also possible to determine when paper and plastic are compressed to make additional room.
Poster
Future Industrial Kitchen: Challenges and Opportunities
Abstract
Large amounts of electricity, water, and food are used every day in Industrial Kitchens (IK). Still, very little attention has been devoted by the research community to this potential source of resource over-consumption. This abstract paper builds on the deployment of sensing technology in three IKs to present the main challenges and potential research directions towards more sustainable IKs.
Presentation Slides
Electricity Consumption Data Sets: Pitfalls and Opportunities
Abstract
Real-world data sets are crucial to develop and test signal processing and machine learning algorithms to solve energy-related problems.
Their scope and data resolution is, however, often limited to the means required to fulfill the experimenters’ objectives and moreover governed by personal experience, budgetary and time constraints, and the availability of equipment.
As a result, numerous differences between data sets can be observed, e.g., regarding their sampling rates, the number of sensors deployed, their amplitude resolutions, storage formats, or the availability and extent of ground-truth annotations.
This heterogeneity poses a significant problem for researchers intending to comparatively use data sets because of the required data conversion, re-sampling, and adaptation steps.
In short, there is a lack of widely agreed best practices for designing, deploying, and operating electrical data collection systems.
We address this limitation by dissecting the collection methodologies used in existing data sets.
By offering recommendations for data collection, data storage, and data provision, we intend to foster the creation of data sets with increased usability and comparability, and thus a greater benefit to the community.
Presentation Slides
On the Value Proposition of Battery Energy Storage in Self-Consumption Only Scenarios: A Case-Study in Madeira Island
Abstract
This paper presents a Techno-Economic assessment of the value proposition of introducing battery energy storage in the Madeira Island electric grid, where only micro-production for self-consumption is currently allowed. The evaluation was conducted against two local micro-producers using one year of energy consumption and solar PV production measurements. The assessments considered three different pairs of battery capacity/inverter size, and the outputs analyzed considering self-consumption, self-sufficiency, and energy costs. The results show that despite the increase in self-consumption and self-sufficiency, the value proposition of battery energy storage is still considerably low even considering a massive decrease in the costs of storage. Furthermore, the results also suggest that given the small size of the solar PV installations, inverters with half the size of the installed PV capacity represent the best value for money.
Poster
NILMPEds: A Performance Evaluation Dataset for Event Detection Algorithms in Non-Intrusive Load Monitoring
dsCleaner: A Python Library to Clean, Preprocess and Convert Non-Intrusive Load Monitoring Datasets
The Acceptance of Energy Monitoring Technologies – The Case of Local Prosumers
Abstract
With transformations happening in the electricity sector, we need to ensure consumers have access to updated and correct information to accompany such changes. Consumers need to understand technologies available to them but also, learn how to use them to optimize their personal investment in such types of equipment. In this paper, we explore how a group of local prosumers has adopted energy monitoring technologies, their day-to-day strategies, and expectations when handling such systems. We studied 11 prosumers and the technologies they have used for three years, evaluated their satisfaction with the feedback provided and analyzed how a more modern visualization of their energy practices was introduced and adopted into their daily lives. We conducted interviews and questionnaires to evaluate their engagement with these tools. This initial work suggests this particular group of users have already a high level of knowledge about their systems, and as a result, have integrated these into their routines. However, more support would be needed from other local actors to help them reach more benefits and as such, more satisfaction as consumers. We conclude by reflecting on barriers that need to be addressed to increase user satisfaction with these systems.
Energy Storage in Madeira, Portugal: Co-optimizing for Arbitrage, Self-Sufficiency, Peak Shaving and Energy Backup
MyTukxi: Low Cost Smart Charging for Small Scale EVs
Extended Abstract
CHI’19 Extended Abstracts, May 4–9, 2019, Glasgow, Scotland UK
Publication year: 2019
A Mouse (H)Over a Hotspot Survey: An Exploration of Patterns of Hesitation through Cursor Movement Metrics
Extended Abstract
CHI’19 Extended Abstracts, May 4–9, 2019, Glasgow, Scotland UK
Publication year: 2019
Abstract:
This paper presents the results of an empirical exploration of 10 cursor movement metrics designed to measure respondent hesitation in online surveys. As a use case, this work considers an online survey aimed at exploring how people gauge the electricity consumption of domestic appliances. The cursor metrics were derived computationally from the mouse trajectories when rating the consumption of each appliance and analyzed using Multidimensional Scaling, Jenks Natural Breaks, and the Jaccard Similarity Index techniques. The results show that despite the fact that the metrics measure different aspects of the mouse trajectories, there is an agreement with respect to the appliances that generated higher levels of hesitation. The paper concludes with an outline of future work that should be carried out in order to further understand how cursor trajectories can be used to measure respondent hesitation.
Poster:
Reproducible Research:
Data and scripts will be added soon.
A global monitoring system for electricity consumption and production of household roof-top PV systems in Madeira
Journal
Neural Computing and Applications, Oct. 2018; doi: 10.1007/s00521-018-3832-3
Publication year: 2018
Highlights of ES2DE and IWOBI 2017: extended versions of selected best papers
Implementation Strategy of Convolution Neural Networks on Field Programmable Gate Arrays for Appliance Classification Using the Voltage and Current (V-I) Trajectory
On the Challenges of Charging Electric Vehicles in Domestic Environments
Poster Abstract
Proceedings of ACM e-Energy 2018, Pages 225-226. Karlsruhe, Germany
Publication year: 2018
Abstract
This poster abstract presents a case study of charging Electric Vehicles (EVs) at home, taking into consideration the household power consumption and the vehicle driving routines of the residents. It reveals some challenges of charging EVs in the household and highlights the importance of proper charging scheduling in order to avoid potential tripping of the household circuit breaker.
Poster
Madeira Pilot User Acceptance Report of the Initial Smart Meter Deployment
Technical Report
H2020 SMILE, EUROPEAN COMMISSION, Funchal, Portugal, Technical report 4.4, Jun. 2018.
Publication year: 2018
Data Collection, Modelling, Simulation and Decision
Technical Report
H2020 SMILE, EUROPEAN COMMISSION, Funchal, Portugal, Technical report 4.3, Jun. 2018
Publication year: 2018
Madeira Pilot Infrastructure Preparation and Kick-off
Technical Report
H2020 SMILE, EUROPEAN COMMISSION, Funchal, Portugal, Technical report 4.2, Jun. 2018
Publication year: 2018
Madeira Pilot Case Study Specification and Assessment
Technical Report
H2020 SMILE, EUROPEAN COMMISSION, Funchal, Portugal, Technical report 4.1, Oct. 2017.
Publication year: 2017
Proceedings of the 2017 Sustainable Internet and ICT for Sustainability (SustainIT)
Collection
IFIP Conference on Sustainable Internet and ICT for Sustainability
Publication year: 2017
Performance Evaluation in Non-Intrusive Load Monitoring: datasets, metrics and tools - a review
Data Storage and Maintenance Challenges: The Case of Advanced Metering Infrastructure Systems
Conference
Proceedings of the 5th International Conference on ICT4S, Toronto, Canada
Publication year: 2018
An Experimental Comparison of Performance Metrics for Event Detection Algorithms in NILM
Workshop
Proceedings of the 4th International Workshop on Non-Intrusive Load Monitoring
Publication year: 2018
A Mouse over a Hotspot Survey: An exploration of perceptions of electricity consumption and patterns of indecision
Engineering and Deploying a Hardware and Software Platform to Collect and Label Non-Intrusive Load Monitoring Datasets
Abstract
Current approaches for collecting and labeling Non- Intrusive Load Monitoring (NILM) datasets still rely heavily on a lengthy and error-prone manual inspection of the whole dataset. Consequently, it is still difficult to find fully labeled datasets that could help furthering, even more, the research in this field. In an attempt to overcome this situation, we propose a hardware and software platform to collect and label NILM sensor data in a semi-automatic labeling fashion. Our platform combines aggregate and plug-level smart-meters to measure consumption data, software algorithms to automatically detect changes in the different monitored loads and a graphical user interface where the end-user can supervise the labeling process. In this paper, we describe the different components that comprise our platform. We also present the results of one live deployment that was performed to test the feasibility of our approach. The results of the deployment show that our system was capable of explaining about 82% of the aggregate load, and automatically detect 94% of the power transitions in the plug-level loads.
Resources
Software (GitLab Repository)
Data (SustDataED Public Dataset)
Developing and Evaluating a Probabilistic Event Detector for Non-Intrusive Load Monitoring
EMD-DF: A Data Model and File Format for Energy Monitoring and Disaggregation Datasets
A Comparison of Performance Metrics for Event Classification in Non-Intrusive Load Monitoring
SustDataED: A Public Dataset for Electric Energy Disaggregation Research
Poster Abstract
Proceedings of ICT for Sustainability 2016. Amsterdam, The Netherlands
Publication year: 2016
Towards using Low-Cost Opportunistic Energy Sensing for Promoting Energy Conservation
What-a-Watt: Exploring Electricity Production Literacy Through a Long Term Eco-Feedback Study
Towards Systematic Performance Evaluation of Non-Intrusive Load Monitoring Algorithms and Systems
EnerSpectrum: Exposing the source of energy through plug-level eco-feedack
Semi-Automatic Labeling for Non-Intrusive Load Monitoring Datasets
Understanding families’ motivations for sustainable behaviors
Show Me or Tell Me: Designing Avatars for Feedback
SustData: A Public Dataset for ICT4S Electric Energy Research
SURF and SURF-PI: A File Format and API for Non-Intrusive Load Monitoring Public Datasets
Poster Abstract
Proceedings of ACM e-Energy 2014, Pages 225-226. Cambridge, UK
Publication year: 2014
What-a-Watt : Where does my electricity comes from?
WattsBurning on my mailbox: a tangible art inspired eco-feedback visualization for sharing energy consumption
WATTSBurning: design and evaluation of an innovative eco-feedback system
Understanding the Limitations of Eco-feedback: a One Year Long-term Study
Towards Automating the Performance Evaluation of Non-Intrusive Load Monitoring Systems
Workshop
Adjunct Proceedings of ICT for Sustainability 2013. Zurich, Switzerland
Publication year: 2013
Low cost framework for non-intrusive home energy monitoring and research
HomeTree – An Art Inspired Mobile Eco-feedback Visualization
A long-term study of energy eco-feedback using non-intrusive load monitoring
Poster Abstract
Adjunct Proceedings of Persuasive Technology 2012. Linköping, Sweden
Publication year: 2012