PhD Thesis Overview

The transition to low-carbon energy, reinforced by international agreements, demands greater flexibility in the electric grid to effectively integrate renewable energy sources. In this context, the widely deployed smart meters provide precise and time-stamped household electricity consumption data, serving as a key enabler for improving grid flexibility. However, their low temporal resolution (typically one reading every 30 minutes) limits their utility for fine-grained analysis, particularly in extracting detailed information on individual appliance consumption. This thesis introduces innovative deep learning-based approaches to analyze these time series and address the challenges of appliance detection and energy usage tracking in households.

After establishing the theoretical foundations and reviewing the state of the art in time series analysis and Non-Intrusive Load Monitoring (NILM), this work first focuses on detecting appliance presence from low-frequency aggregated data. An evaluation of multiple classification techniques highlights the superiority of deep learning-based approaches. First, we investigate various time series classification methods and show that deep learning significantly outperforms traditional approaches for detecting whether a household owns a particular appliance. We then introduce ADF&TransApp, a detection framework leveraging Transformers pre-trained on large volumes of unlabeled data, thus making full use of the extensive consumption datasets available. Next, we address the localization of appliance activation periods. We propose CamAL, a weakly supervised approach combining convolutional networks and explainability techniques. A key innovation of our solution is its ability to be trained solely on appliance ownership information, significantly reducing the need for annotated data. To make this approach accessible, we developed DeviceScope, an interactive tool that highlights appliance signatures in consumption data, providing clearer insights for both consumers and electricity providers. Finally, to estimate individual appliance consumption, we introduce NILMFormer, a Transformer-based architecture incorporating a normalization mechanism tailored to the NILM problem. This design effectively handles the intrinsic variations in consumption data, addressing the challenge posed by their non-stationary nature.

The work conducted in this thesis has led to the large-scale operational deployment of the proposed solutions, demonstrating their relevance for optimized energy management and their active contribution to the energy transition.