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Leveraging Machine Learning for Sustainable and Self-sufficient Energy Communities

Workshop
Anthony Faustine, Lucas Pereira, Daniel Ngondya, Loubna Benabbou
Tackling Climate Change with Machine Learning (NeurIPS 2020 Workshops)
Publication year: 2020

UNet-NILM: A Deep Neural Network for Multi-tasks Appliances State Detection and Power Estimation in NILM

Workshop
Anthony Faustine, Lucas Pereira, Hafsa Bousbiat, Shridhar Kulkarni
Proceedings of NILM '20, November 18, 2020, Virtual Event, Japan
Publication year: 2020

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

Workshop
João Gois, Christoph Klemenjak, Lucas Pereira
Proceedings of NILM '20, November 18, 2020, Virtual Event, Japan
Publication year: 2020

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.

Energy Storage Optimization for Grid Reliability

Workshop
Md Umar Hashmi, Deepjyoti Deka, Lucas Pereira, Ana Bušić
Proceedings of ACM e-Energy 2020, Pages 516–522. Virtual Event
Publication year: 2020

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

Preprint

An Experimental Comparison of Performance Metrics for Event Detection Algorithms in NILM

Workshop
Lucas Pereira, Nuno Nunes
Proceedings of the 4th International Workshop on Non-Intrusive Load Monitoring
Publication year: 2018

Towards using Low-Cost Opportunistic Energy Sensing for Promoting Energy Conservation

Workshop
Nuno J. Nunes, Lucas Pereira, Valentina Nisi
Adjunct Proceedings of Interact 2015. Bamberg, Germany
Publication year: 2015

Towards Systematic Performance Evaluation of Non-Intrusive Load Monitoring Algorithms and Systems

Workshop
Lucas Pereira, Nuno J. Nunes
Proceedings of SustainIT 2015. Madrid, Spain
Publication year: 2015

Towards Automating the Performance Evaluation of Non-Intrusive Load Monitoring Systems

Workshop
Lucas Pereira
Adjunct Proceedings of ICT for Sustainability 2013. Zurich, Switzerland
Publication year: 2013