Forecasting Anonymized Electricity Load Profiles
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915098645233664 |
|---|---|
| author | Fernandez, Joaquin Delgado Menci, Sergio Potenciano Magitteri, Alessio |
| author_facet | Fernandez, Joaquin Delgado Menci, Sergio Potenciano Magitteri, Alessio |
| contents | In the evolving landscape of data privacy, the anonymization of electric load profiles has become a critical issue, especially with the enforcement of the General Data Protection Regulation (GDPR) in Europe. These electric load profiles, which are essential datasets in the energy industry, are classified as personal behavioral data, necessitating stringent protective measures. This article explores the implications of this classification, the importance of data anonymization, and the potential of forecasting using microaggregated data. The findings underscore that effective anonymization techniques, such as microaggregation, do not compromise the performance of forecasting models under certain conditions (i.e., forecasting aggregated). In such an aggregated level, microaggregated data maintains high levels of utility, with minimal impact on forecasting accuracy. The implications for the energy sector are profound, suggesting that privacy-preserving data practices can be integrated into smart metering technology applications without hindering their effectiveness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_06237 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Forecasting Anonymized Electricity Load Profiles Fernandez, Joaquin Delgado Menci, Sergio Potenciano Magitteri, Alessio Cryptography and Security Artificial Intelligence Machine Learning I.2.0; J.2.7 In the evolving landscape of data privacy, the anonymization of electric load profiles has become a critical issue, especially with the enforcement of the General Data Protection Regulation (GDPR) in Europe. These electric load profiles, which are essential datasets in the energy industry, are classified as personal behavioral data, necessitating stringent protective measures. This article explores the implications of this classification, the importance of data anonymization, and the potential of forecasting using microaggregated data. The findings underscore that effective anonymization techniques, such as microaggregation, do not compromise the performance of forecasting models under certain conditions (i.e., forecasting aggregated). In such an aggregated level, microaggregated data maintains high levels of utility, with minimal impact on forecasting accuracy. The implications for the energy sector are profound, suggesting that privacy-preserving data practices can be integrated into smart metering technology applications without hindering their effectiveness. |
| title | Forecasting Anonymized Electricity Load Profiles |
| topic | Cryptography and Security Artificial Intelligence Machine Learning I.2.0; J.2.7 |
| url | https://arxiv.org/abs/2501.06237 |