Publications

2024

1. MLPQF

   Efficiency through Simplicity: MLP-based Approach for Net-Load Forecasting with Uncertainty Estimates in Low-Voltage Distribution Networks

   Faustine, Anthony, Nunes, Nuno Jardim, and Pereira, Lucas

   IEEE Transactions on Power Systems 2024

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   Power demand forecasting is becoming a crucial tool for the planning and operation of Low Voltage (LV) distribution systems. Most importantly, the high penetration of Photovoltaics (PV) power generation as part of Distributed Energy Resource (DER)s has transformed the power demand forecasting problem at the distribution level into net-load forecasting. This paper introduces a novel and scalable approach to probabilistic forecasting at LV substations with PV generation. It presents a multi-variates probabilistic forecasting approach, leveraging Quantile Regression (QR). The proposed architecture uses a computationally efficient feed-forward neural net to capture the complex interaction between the historical load demands and covariate variables such as solar irradiance. It is empirically demonstrated that the proposed method can efficiently produce well-calibrated forecasts, both auto-regressively or in a single forward pass. Furthermore, a benchmark against four state-of- the-art forecasting approaches show that the proposed approach offers a desirable trade-off between forecasting accuracies, calibrated uncertainty, and computation complexity

2023

1. IndustrialNILM

   Applying Symmetrical Component Transform for Industrial Appliance Classification in Non-Intrusive Load Monitoring

   Faustine, Anthony, and Pereira, Lucas

   In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2023

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   Industrial loads offer challenges for Non-intrusive Load Monitoring (NILM), such as phase imbalance associated with 3-phase lines. However, very little NILM research has been developed so far with this respect. This work presents a load recognition technique for NILM applying low complexity Fortesque Transform (FT). The FT decomposes the unbalanced 3-phase current waveform extracted from 3-phase aggregate power measurements to balance the given load. The 3-phases current waveform is transformed into an image-like representation using a compressedeuclidean distance matrix to improve the recognition ability further. The image representation is used as input to Convolutional Neural Network (CNN) classifier to learn the patterns of labeled data. Experimental evaluation of the industrial aggregated dataset shows that FT improves recognition performance.

2022

1. MLABEL

   Appliance recognition with combined single- and multi-label approaches

   Manca, Marco Manolo, Faustine, Anthony, and Pereira, Lucas

   In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation 2022

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   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 recognising 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 which 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 mult-label learning strategies for appliance recognition provides improved classification results with an F-score of 0.91.

2. DeepNLMTK

   Unlocking the Full Potential of Neural NILM: On Automation, Hyperparameters and Modular Pipelines

   IEEE Transactions on Industrial Informatics 2022

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   Non-Intrusive Load Monitoring (nilm) techniques are increasingly becoming a key instrument for identifying the power consumption of individual appliances based on a single metering point. Particularly Deep learning models are gaining interest in this regard. However, the challenges brought by NILM datasets and the non-availability of common experimental guidelines tend to compromise comparison, research transparency and replicability. The limited adoption of efficient research instruments and a lack of best practices guidelines contribute in huge part to this problem, where no features, encouraging standardised formats for benchmarking and results sharing, are offered. To address these issues, we first present a brief overview of recent best practices for Deep Learning (DL) and highlight how deep NILM research can benefit from these practices. Furthermore, we suggest a novel open-source toolkit leveraging these practices: Deep-NILMTK. The proposed toolkit offers a common testing bed for NILM algorithms independently of the underlying deep learning framework with a modular NILM pipeline that can easily be customised. Furthermore, Deep-NILMTK introduces the concept of Experiment Templating to offer pre-designed experiments allowing to enhance research efficiency

3. FPSeq2Q

   FPSeq2Q: Fully Parameterized Sequence to Quantile Regression for Net-Load Forecasting with Uncertainty Estimates

   Faustine, Anthony, and Pereira, Lucas

   IEEE Transactions on Smart Grid 2022

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   The increased penetration of Renewable Energy Sources (RES) as part of a decentralized and distributed power system makes net-load forecasting a critical component in the planning and operation of power systems. However, compared to the transmission level, producing accurate short-term netload forecasts at the distribution level is complex due to the small number of consumers. Moreover, owing to the stochastic nature of RES, it is necessary to quantify the uncertainty of the forecasted net-load at any given time, which is critical for the real-world decision process. This work presents parameterized deep quantile regression for short-term probabilistic net-load forecasting at the distribution level. To be precise, we use a Deep Neural Network (DNN) to learn both the quantile fractions and quantile values of the quantile function. Furthermore, we propose a scoring metric that reflects the trade-off between predictive uncertainty performance and forecast accuracy. We evaluate the proposed techniques on historical real-world data from a lowvoltage distribution substation and further assess its robustness when applied in real-time. The experiment’s outcomes show that the resulting forecasts from our approach are well-calibrated and provide a desirable trade-off between forecasting accuracies and predictive uncertainty performance that are very robust even when applied in real-time.

2021

1. Watt’supHome

   Watt’s up at Home? Smart Meter Data Analytics from a Consumer-Centric Perspective

   Völker, Benjamin, Reinhardt, Andreas, Faustine, Anthony, and Pereira, Lucas

   Energies 2021

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   The key advantage of smart meters over traditional metering devices is their ability to transfer consumption information to remote data processing systems. Besides enabling the automated collection of a customer’s electricity consumption for billing purposes, the data collected by these devices makes the realization of many novel use cases possible. However, the large majority of such services are tailored to improve the power grid’s operation as a whole. For example, forecasts of household energy consumption or photovoltaic production allow for improved power plant generation scheduling. Similarly, the detection of anomalous consumption patterns can indicate electricity theft and serve as a trigger for corresponding investigations. Even though customers can directly influence their electrical energy consumption, the range of use cases to the users’ benefit remains much smaller than those that benefit the grid in general. In this work, we thus review the range of services tailored to the needs of end-customers. By briefly discussing their technological foundations and their potential impact on future developments, we highlight the great potentials of utilizing smart meter data from a user-centric perspective. Several open research challenges in this domain, arising from the shortcomings of state-of-the-art data communication and processing methods, are furthermore given. We expect their investigation to lead to significant advancements in data processing services and ultimately raise the customer experience of operating smart meters.

2. AWRGNILM

   Adaptive Weighted Recurrence Graphs for Appliance Recognition in Non-Intrusive Load Monitoring

   Faustine, Anthony, Pereira, Lucas, and Klemenjak, Christoph

   IEEE Transactions on Smart Grid 2021

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   To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task. This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep neural network architectures such as Convolutional Neural Networks for appliance recognition. Our approach transforms one cycle per activation current into an weighted recurrence graph and treats the associated hyper-parameters as learn-able parameters. We evaluate our technique on two energy datasets, the industrial dataset LILACD and the residential PLAID dataset. The outcome of our experiments shows that transforming current waveforms into weighted recurrence graphs provides a better feature representation and thus, improved classification results. It is concluded that our approach can guarantee uniqueness of appliance features, leading to enhanced generalisation abilities when compared to the widely researched V-I image features. Furthermore, we show that the initialisation parameters of the AWRG’s have a significant impact on the performance and training convergence.

2020

1. WRGNILM

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

   Faustine, Anthony, and Pereira, Lucas

   Energies 2020

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   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.

2. MLABELILM

   Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

   Faustine, Anthony, and Pereira, Lucas

   Energies 2020

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   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.

3. UNETNILM

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

   Faustine, Anthony, Pereira, Lucas, Bousbiat, Hafsa, and Kulkarni, Shridhar

   In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring 2020

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   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 UK-DALE dataset suggests promising performance against traditional single-task learning.

4. ML4EC

   Leveraging Machine learning for Sustainable and Self-sufficient Energy Communities

   Anthony Faustine, Lucas Pereira, Ngondya, Daniel, and Benabbou, Loubna

   In NeurIPS 2020 Workshop Tackling Climate Change with Machine Learning 2020

2017

1. NILMSurvey

   A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem

   Faustine, Anthony, Mvungi, Nerey Henry, Kaijage, Shubi, and Michael, Kisangiri

   CoRR 2017

2014

1. WSNWQM

   Wireless Sensor Networks for Water Quality Monitoring and Control within Lake Victoria Basin: Prototype Development

   Faustine, Anthony, Mvuma, Aloys N., Mongi, Hector J., Gabriel, Maria C., Tenge, Albino J., and Kucel, Samuel B.

   Wireless Sensor Network 2014
2. MobSensing

   Ubiquitous Mobile Sensing for Water Quality Monitoring and Reporting within Lake Victoria Basin

   Faustine, Anthony, and Mvuma, Aloys N.

   Wireless Sensor Network 2014