联邦学习在住宅能源预测中的性能评价

Eugenia Petrangeli, N. Tonellotto, C. Vallati
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引用次数: 4

摘要

短期能源消费预测在能源生产、运输和分配规划中起着重要的作用。随着分散式自发电系统在住宅社区的广泛应用,短期负荷预测有望以分布式方式进行,以保护隐私并确保及时反馈以进行配电网的重新配置。在这种情况下,边缘计算有望成为一种使能技术,以确保分散的数据收集、管理、处理和交付。与此同时,联邦学习是一种新兴的范例,自然适合于这样的边缘计算环境,为时间序列预测提供了人工智能驱动和隐私保护的解决方案。在本文中,我们提出了一个性能评估不同的联邦学习配置导致不同隐私级别的预测住宅能源消耗与实际智能电表收集的数据。为此,使用Flower(一种流行的联邦学习框架)和真实的能源消耗数据进行了不同的实验。我们的结果使我们能够证明这种方法的可行性,并研究数据隐私和预测准确性之间的权衡,这是系统为最终用户提供服务质量的特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Evaluation of Federated Learning for Residential Energy Forecasting
Short-term energy-consumption forecasting plays an important role in the planning of energy production, transportation and distribution. With the widespread adoption of decentralised self-generating energy systems in residential communities, short-term load forecasting is expected to be performed in a distributed manner to preserve privacy and ensure timely feedback to perform reconfiguration of the distribution network. In this context, edge computing is expected to be an enabling technology to ensure decentralized data collection, management, processing and delivery. At the same time, federated learning is an emerging paradigm that fits naturally in such an edge-computing environment, providing an AI-powered and privacy-preserving solution for time-series forecasting. In this paper, we present a performance evaluation of different federated-learning configurations resulting in different privacy levels to the forecast residential energy consumption with data collected by real smart meters. To this aim, different experiments are run using Flower (a popular federated learning framework) and real energy consumption data. Our results allow us to demonstrate the feasibility of such an approach and to study the trade-off between data privacy and the accuracy of the prediction, which characterizes the quality of service of the system for the final users.
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