Privacy-preserving Federated Learning for Residential Short Term Load Forecasting
| Autor: | Joaquín Delgado Fernández, Sergio Potenciano Menci, Chul Min Lee, Alexander Rieger, Gilbert Fridgen |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning General Energy Artificial Intelligence (cs.AI) Computer Science - Artificial Intelligence Mechanical Engineering FOS: Electrical engineering electronic engineering information engineering Building and Construction Systems and Control (eess.SY) Management Monitoring Policy and Law Electrical Engineering and Systems Science - Systems and Control Machine Learning (cs.LG) |
| Popis: | With high levels of intermittent power generation and dynamic demand patterns, accurate forecasts for residential loads have become essential. Smart meters can play an important role when making these forecasts as they provide detailed load data. However, using smart meter data for load forecasting is challenging due to data privacy requirements. This paper investigates how these requirements can be addressed through a combination of federated learning and privacy preserving techniques such as differential privacy and secure aggregation. For our analysis, we employ a large set of residential load data and simulate how different federated learning models and privacy preserving techniques affect performance and privacy. Our simulations reveal that combining federated learning and privacy preserving techniques can secure both high forecasting accuracy and near-complete privacy. Specifically, we find that such combinations enable a high level of information sharing while ensuring privacy of both the processed load data and forecasting models. Moreover, we identify and discuss challenges of applying federated learning, differential privacy and secure aggregation for residential short-term load forecasting. |
| Databáze: | OpenAIRE |
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