The inclusion of intermittent and renewable energy sources has increased the importance of demand forecasting in power systems. Smart meters can play a critical role in demand forecasting due to the measurement granularity they provide. Despite their virtue, smart meters used for forecasting face some constraints as consumers' privacy concerns, reluctance of utilities and vendors to share data with competitors or third parties, and regulatory constraints. This paper examines a collaborative machine learning method, federated learning extended with privacy preserving techniques for short-term demand forecasting using smart meter data as a solution to the previous constraints. The combination of privacy preserving techniques and federated learning enables to ensure consumers' confidentiality concerning both their data, the models generated using it (Differential Privacy), and the communication mean (Secure Aggregation). To evaluate this paper's collaborative secure federated learning setting, we explore current literature to select the baseline for our simulations and evaluation. We simulate and evaluate several scenarios that explore how traditional centralized approaches could be projected in the direction of a decentralized, collaborative and private system. The results obtained over the evaluations provided decent performance and in a privacy setting using differential privacy almost perfect privacy budgets (1.39,$10e^{-5}$) and (2.01,$10e^{-5}$) with a negligible performance compromise.
翻译:将间歇和可再生能源纳入电力系统增加了需求预测的重要性; 智能测量仪因其提供的测量颗粒而可以在需求预测中发挥关键作用; 尽管其优点,用于预测的智能测量仪面临一些制约因素,如消费者隐私关切、公用事业和供应商不愿与竞争者或第三方分享数据以及监管限制等; 本文审查了一种协作的机器学习方法,结合隐私保护技术,利用智能计量仪数据为短期需求预测提供隐私保护技术; 将隐私保护技术和联合学习相结合,能够确保消费者对其数据、使用这些数据产生的模型(差异隐私权)和通信手段(保密聚合)的保密性。 为了评估本文件的合作安全封存学习环境,我们探索目前的文献,为我们的模拟和评价选择基线。 我们模拟并评价了几种设想,探索如何将传统的集中方法预测到分散、协作和私人系统的方向上。 通过这些评价取得的结果提供了体面的业绩,并在使用几乎完美的隐私预算(1.39美元,10欧元-5美元)和最低性业绩(2美元)和(2.01美元)的保密性。