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Accurate Federated Learning with Uncertainty Quantification for DER Forecasting Applied to Smart Grids Planning and Operation: The ALAMO Vision

Workshop
Lucas Pereira, Vineet Nair, Anuradha AnnaSwamy, Bruno Dias, Hugo Morais
CIRED 2024
Publication year: 2024

Abstract

This paper presents the ALAMO vision, which is a research project that aims at developing technologies to facilitate the planning and operation of power grids with very high penetration of Distributed Energy Resources, while at the same time assuring the privacy of the main actors on the demand side. The ALAMO project will address outstanding research challenges related to the development of accurate forecasting algorithms based on Federated Learning (e.g., forecasting in- front and behind-the-meter PV production); as well as challenges related to producing sharped and well-calibrated quantifications of epistemic and aleatoric uncertainty for such forecasting models. Finally, use-cases will be carefully crafted to understand how FL forecasts and uncertainty estimates can be incorporated into operational planning and operation tools. The use-cases will be demonstrated in virtual and physical testbeds in Portugal, USA, and Brazil.

Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid

Workshop
Vineet Jagadeesan Nair, Lucas Pereira
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning
Publication year: 2022

Abstract

This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients.

Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times

Workshop
Matthew Beveridge, Lucas Pereira
NeurIPS 2022 Workshop on Tackling Climate Change with Machine Learning
Publication year: 2022

Abstract

There are many benefits from the accurate forecasting of Arctic sea ice, however existing models struggle to reliably predict sea ice concentration at long lead times. Many numerical models exist but can be sensitive to initial conditions, and while recent deep learning-based methods improve overall robustness, they either do not utilize temporal trends or rely on architectures that are not performant at learning long-term sequential dependencies. We propose a method of forecasting sea ice concentration using neural circuit policies, a form of continuous time recurrent neural architecture, which improve the learning of long-term sequential dependencies compared to existing techniques and offer the added benefits of adaptability to irregular sequence intervals and high interpretability.

Appliance Recognition with Combined Single- and Multi-label Approaches

Workshop
Manolo Manca, Anthony Faustine, Lucas Pereira
6th International Workshop on Non-Intrusive Load Monitoring (NILM ’22), November 9–10, 2022, Boston, MA, USA; doi: 10.1145/3563357.3566153
Publication year: 2022

Abstract:

The problem of appliance recognition is one of the most relevant issues in the field of Non-Intrusive-Load-Monitoring; its importance has led, in recent years, to the development of innovative techniques to try to solve it. The use of methods such as V-I trajectory, Fryze Theory Decomposition, and Weighted Recurrence Graph have proved effective in recognizing both single (Single Label) and multiple active appliances (Multi-Label). This paper presents a new way of approaching the problem by unifying Single Label and Multi-Label learning paradigms. The proposed approach exploits feature extraction techniques that allow the detection of both activated/deactivated appliances and all active appliances given aggregate current signal. We evaluate the proposed approach on a PLAID dataset. The obtained results indicate combining single-label and multi-label learning strategies for appliance recognition provides improved classification results with an F-score of 0.91.

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