Dr. Giulia Tanoni

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Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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18 July 2022 in IEEE Transactions on Smart Grid

Journal Article

Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring

Giulia Tanoni

Emanuele Principi

Stefano Squartini

Non-Intrusive Load Monitoring consists in estimating the power consumption or the states of the appliances using electrical parameters acquired from a single metering point. State-of-the-art approaches are based on deep neural networks, and for training, they require a significant amount of data annotated at the sample level, defined as strong labels. This paper presents an appliance classification method based on a Convolutional Recurrent Neural Network trained with weak supervision. Learning is formulated as a Multiple-Instance Learning problem, and the network is trained on labels provided for an entire segment of the aggregate power, defined as weak labels. Weak labels are coarser annotations that are intrinsically less costly to obtain compared to strong labels. An extensive experimental evaluation has been conducted on the UK-DALE and REFIT datasets comparing the proposed approach to three benchmark methods. The results obtained for different amounts of strongly and weakly labeled data and mixing UK-DALE and REFIT confirm the effectiveness of weak labels compared to fully supervised and semi-supervised benchmarks methods.

10 July 2024 in Renewable and Sustainable Energy Reviews

Journal Article

Non-Intrusive Load Monitoring in industrial settings: A systematic review

Giulia Tanoni

Emanuele Principi

Stefano Squartini

9 November 2023 in IEEE Transactions on Industrial Informatics

Journal Article

Knowledge Distillation for Scalable Nonintrusive Load Monitoring

Smart meters allow the grid to interface with individual buildings and extract detailed consumption information using nonintrusive load monitoring (NILM) algorithms applied to the acquired data. Deep neural networks, which represent the state of the art for NILM, are affected by scalability issues since they require high computational and memory resources, and by reduced performance when training and target domains mismatched. This article proposes a knowledge distillation approach for NILM, in particular for multilabel appliance classification, to reduce model complexity and improve generalization on unseen data domains. The approach uses weak supervision to reduce labeling effort, which is useful in practical scenarios. Experiments, conducted on U.K.-DALE and REFIT datasets, demonstrated that a low-complexity network can be obtained for deployment on edge devices while maintaining high performance on unseen data domains. The proposed approach outperformed benchmark methods in unseen target domains achieving a $F_{1}$ -score 0.14 higher than a benchmark model 78 times more complex.

1 January 2018 in Procedia Computer Science

Journal Article

A comparative study of machine learning algorithms for physiological signal classification

Giorgio Biagetti

Paolo Crippa

Laura Falaschetti

Giulia Tanoni

Claudio Turchetti

29 August 2022

Conference Paper

A Multiple Instance Regression Approach to Electrical Load Disaggregation

Luca Serafini

Giulia Tanoni

Emanuele Principi

Susanna Spinsante

Stefano Squartini

Non-Intrusive Load Monitoring (NILM) provides detailed information on the consumption of individual appliances in a building and represents an effective method to reduce the electricity consumed in the residential sector. Supervised Deep Learning approaches have achieved the state-of-the-art for NILM but require knowledge of strongly labeled data, i.e., annotated at the sample level. This data is costly to obtain since it requires multiple sensors to measure electrical quantities and the involvement of the end-users. This work proposes a Multiple Instance Regression approach to NILM using a Convolutional Recurrent Neural Network (CRNN) to reduce the amount of strongly labeled data required for training and improve performance. Instances of strongly labeled data are here represented by raw samples of active power and are aggregated into bags containing weak information rep-resented by the average power consumption in a bag. Using this information, the network is trained to disaggregate appliances' power profiles with sample resolution. The results obtained on the UK-DALE dataset demonstrated the approach's effectiveness in reducing the labeling cost and improving the performance: the average Mean Absolute Error reduces by 3.06 W when weak information is used in the CRNN and by 8.88 W compared to the Sequence-to-Point method.

4 June 2023

Conference Paper

Improving Knowledge Distillation for Non-Intrusive Load Monitoring Through Explainability Guided Learning

Djordje Batic

Giulia Tanoni

Lina Stankovic

Vladimir Stankovic

Emanuele Principi

Knowledge distillation (KD) is a machine learning technique widely used in recent years for the task of domain adaptation and complexity reduction. It relies on a Student-Teacher mechanism to transfer the knowledge of a large and complex Teacher network into a smaller Student model. Given the inherent complexity of large Deep Neural Network (DNN) models, and the need for deployment on edge devices with limited resources, complexity reduction techniques have become a hot topic in the Non-intrusive Load Monitoring (NILM) community. Recent literature in NILM has devoted increased effort to domain adaptation and architecture reduction via KD. However, the mechanism behind the transfer of knowledge from the Teacher to the Student is not clearly understood. In this work, we aim to address the aforementioned issue by placing the KD NILM approach in a framework of explainable AI (XAI). We identify the main inconsistency in the transfer of explainable knowledge, and exploit this information to propose a method for improvement of KD through explainability guided learning. We evaluate our approach on a variety of appliances and domain adaptation scenarios and demonstrate that solving inconsistencies in the transfer of explainable knowledge can lead to improvement in predictive performance.

About Me

Giulia Tanoni received the M.Sc. degree cum laude in Biomedical Engineering in 2020 from Università Politecnica delle Marche, Ancona, Italy. In 2020 she undertook the Ph.D. degree in Information Engineering, accomplished in 2024 with excellent judgment and laude, with the Department of Information Engineering at Università Politecnica delle Marche, Ancona, Italy. In 2022, she was a visiting PhD Student with the Department of Electronic & Electrical Engineering of the University of Strathclyde in Glasgow, Scotland, under the supervision of Prof. Vladimir Stankovic to develop strategies based on edge computing and explainable artificial intelligence for non-intrusive load monitoring. She is a member of the IEEE Young Professionals and since 2023 a member of the IEEE CIS Task Force on ’Computational Intelligence in the Energy Domain’ (CI4E) of the IEEE Computational Intelligence Society. Her research interests concern the design of advanced edge-centric non-intrusive load monitoring algorithms based on computational intelligence techniques. In particular, Giulia Tanoni is active in the research of innovative learning methods to create deep learning approaches that meet sustainability requirements and users’ necessity. She also develops strategies for power signal processing, analysis, and management including methodologies such as neural networks and deep learning methods for observable smart energy systems.

Dr. Giulia Tanoni

Dr. Giulia Tanoni

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