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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Seer Grid: Privacy and Utility Implications of Two-Level Load Prediction in Smart Grids
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Seer Grid: Privacy and Utility Implications of Two-Level Load Prediction in Smart Grids

机译：Seer Grid：智能电网中两级负荷预测的隐私和实用程序含义

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摘要

We propose “Seer Grid”, a novel two-level energy consumption prediction framework for smart grids, aimed to decrease the trade-off between privacy requirements (of the customer) and data utility requirements (of the energy company (EC)). The first-level prediction at the household level is performed by each smart meter (SM), and the predicted energy consumption pattern (instead of the actual energy usage data) is reported to a cluster head (CH). Then, a second-level prediction at the neighborhood level is done by the CH which predicts the energy spikes in the neighborhood or cluster and shares it with the EC. Our two-level prediction mechanism is designed such that it preserves the correlation between the predicted and actual energy consumption patterns at the cluster level and removes this correlation in the predicted data communicated by each SM to the CH. This maintains the usefulness of the cluster-level energy consumption data communicated to the EC, while preserving the privacy of the household-level energy consumption data against the CH (and thus the EC). Our evaluation results show that Seer Grid is successful in hiding private consumption patterns at the household-level while still being able to accurately predict energy consumption at the neighborhood-level.
机译：我们提出了“ Seer Grid”，这是一种用于智能电网的新颖的两级能耗预测框架，旨在减少（客户的）隐私要求与（能源公司的（EC））数据公用事业要求之间的权衡。每个智能电表（SM）进行家庭一级的第一级预测，并将预测的能耗模式（而不是实际的能耗数据）报告给簇头（CH）。然后，由CH进行邻域级别的第二级预测，该CH预测邻域或群集中的能量峰值并与EC共享。我们设计了两级预测机制，以使其在群集级别上保留了预测能耗模式与实际能耗模式之间的相关性，并消除了每个SM传递给CH的预测数据中的这种相关性。这保持了传达给EC的集群级能耗数据的有用性，同时保留了家庭级能耗数据相对于CH（以及EC）的私密性。我们的评估结果表明，Seer Grid成功地隐藏了家庭层面的私人消费模式，同时仍然能够准确预测社区层面的能源消耗。

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