Unsupervised Clustering and Explainable AI for Unveiling Behavioral Variations Across Time in Home-Appliance Generated Data

Tolas, Ramona; Portase, Raluca; Lemnaru, Camelia; Dinsoreanu, Mihaela; Potolea, Rodica

doi:10.1007/978-3-031-48316-5_17

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…This is an entire research topic that deserves special attention. In [44], we present an unsupervised method that uses an explainable AI component to discover when a certain pattern occurs in time. The methodology can be extended to the energy consumption patterns extracted with the current approach.…”

Section: Applicabilitymentioning

confidence: 99%

From Individual Device Usage to Household Energy Consumption Profiling

Tolas,

Portase,

Potolea

2024

Electronics

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This paper presents a novel approach for electricity consumption profiling in households through the fusion of usage data for individual smart devices. The novelty of the approach consists of leveraging the data representing the usage of individual appliances rather than using direct measurements of energy consumption. Our methodology focuses on merging signals representing the interaction of the user with the device to compute patterns in the total energy consumption per household. Subsequently, we apply data mining techniques—specifically, unsupervised clustering—to analyze the resulting time-series data representing daily energy consumption. Through this approach, we aim to identify and characterize patterns in energy usage within households, enabling insights for energy optimization strategies and resource allocation. This information can be further used in practical tasks, such as flattening energy consumption. The proposed approach offers an alternative to the direct measurement of energy usage, considering the potential for sensor failure or malfunction. This underscores the importance of implementing a complementary method for verifying and validating energy consumption data.

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Section: Applicabilitymentioning

confidence: 99%

From Individual Device Usage to Household Energy Consumption Profiling

Tolas,

Portase,

Potolea

2024

Electronics

Self Cite

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GeMSyD: Generic Framework for Synthetic Data Generation

Tolas,

Portase,

Potolea

2024

Data

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In the era of data-driven technologies, the need for diverse and high-quality datasets for training and testing machine learning models has become increasingly critical. In this article, we present a versatile methodology, the Generic Methodology for Constructing Synthetic Data Generation (GeMSyD), which addresses the challenge of synthetic data creation in the context of smart devices. GeMSyD provides a framework that enables the generation of synthetic datasets, aligning them closely with real-world data. To demonstrate the utility of GeMSyD, we instantiate the methodology by constructing a synthetic data generation framework tailored to the domain of event-based data modeling, specifically focusing on user interactions with smart devices. Our framework leverages GeMSyD to create synthetic datasets that faithfully emulate the dynamics of human–device interactions, including the temporal dependencies. Furthermore, we showcase how the synthetic data generated using our framework can serve as a valuable resource for machine learning practitioners. By employing these synthetic datasets, we perform a series of experiments to evaluate the performance of a neural-network-based prediction model in the domain of smart device interaction. Our results underscore the potential of synthetic data in facilitating model development and benchmarking.

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Unsupervised Clustering and Explainable AI for Unveiling Behavioral Variations Across Time in Home-Appliance Generated Data

Cited by 2 publications

References 21 publications

From Individual Device Usage to Household Energy Consumption Profiling

From Individual Device Usage to Household Energy Consumption Profiling

GeMSyD: Generic Framework for Synthetic Data Generation

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