Secure Federated Learning for Residential Short Term Load Forecasting

• URL: http://arxiv.org/abs/2111.09248v1
• Date: Wed, 17 Nov 2021 17:27:59 GMT
• Title: Secure Federated Learning for Residential Short Term Load Forecasting
• Authors: Joaquin Delgado Fernandez, Sergio Potenciano Menci, Charles Lee, Gilbert Fridgen
• Abstract summary: This paper examines a collaborative machine learning method for short-term demand forecasting using smart meter data. The methods evaluated take into account several scenarios that explore how traditional centralized approaches could be projected in the direction of a decentralized, collaborative and private system.
• Score: 0.34123736336071864
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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. Consumers' privacy concerns, reluctance of utilities and vendors to share data with competitors or third parties, and regulatory constraints are some constraints smart meter forecasting faces. This paper examines a collaborative machine learning method for short-term demand forecasting using smart meter data as a solution to the previous constraints. Privacy preserving techniques and federated learning enable to ensure consumers' confidentiality concerning both, their data, the models generated using it (Differential Privacy), and the communication mean (Secure Aggregation). The methods evaluated take into account 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 almost perfect privacy budgets (1.39,$10e^{-5}$) and (2.01,$10e^{-5}$) with a negligible performance compromise.

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