FedGrid: A Secure Framework with Federated Learning for Energy Optimization in the Smart Grid

Abstract

In the contemporary energy landscape, power generation comprises a blend of renewable and non-renewable resources, with the major supply of electrical energy fulfilled by non-renewable sources, including coal and gas, among others. Renewable energy resources are challenged by their dependency on unpredictable weather conditions. For instance, solar energy hinges on clear skies, and wind energy relies on consistent and sufficient wind flow. However, as a consequence of the finite supply and detrimental environmental impact associated with non-renewable energy sources, it is required to reduce dependence on such non-renewable sources. This can be achieved by precisely predicting the generation of renewable energy using a data-driven approach. The prediction accuracy for electric load plays a very significant role in this system. If we have an appropriate estimate of residential and commercial load, then a strategy could be defined for the efficient supply to them by renewable and non-renewable energy sources through a smart grid, which analyzes the demand-supply and devises the supply mechanism accordingly. Predicting all such components, i.e., power generation and load forecasting, involves a data-driven approach where sensitive data (such as user electricity consumption patterns and weather data near power generation setups) is used for model training, raising the issue of data privacy and security concerns. Hence, the work proposes Federated Smart Grid (FedGrid), a secure framework that would be able to predict the generation of renewable energy and forecast electric load in a privacy-oriented approach through federated learning. The framework collectively analyzes all such predictive models for efficient electric supply.

Resilient Electricity Load Forecasting Network with Collective Intelligence Predictor for Smart Cities

“…Robust Model [35] ✓ × Transformer [33,34] △ × Forecasting Network ✓ △ Federated Learning [38][39][40] × ✓ Boosting Ensemble Learning [36,37] ✓ × Temporal Convolutional Network [31,32] △ × Recurrent Neural Network Derivatives [30] △ ×…”

“…Although several distributed computing methods could solve the SPoF vulnerability when the server hosting the forecasting system is taken offline by the DDoS attacks, recent studies show that federated learning is the favorable method due to the capability to train the model in independent sessions without sharing the datasets that may contain sensitive information [38][39][40]. However, in addition to the inefficiency and data heterogeneity negatively impacting the accuracy mentioned in Section 1, the studies in federated learning did not consider the countermeasures against MV.…”