FedForecast: A federated learning framework for short-term probabilistic individual load forecasting in smart grid

Liu, Yi-Xing; Dong, Zhen; Бо, Лю; Xu, Yiqiao; Ding, Zhengtao

doi:10.1016/j.ijepes.2023.109172

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…Hence, for the interoperability of the proposed framework within existing systems, it is necessary that clients should use devices with good computational capabilities built in so that the current system can be integrated. Keeping these aspects in consideration, the work [10] proposes FedForecast framework, which performs federated-learning-based individual load forecasting by ensuring privacy and utilizing edge computing resources. The authors utilized the PecanStreet dataset for training the edge devices while addressing the issue of system heterogeneity.…”

Section: Literature Reviewmentioning

confidence: 99%

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

Gupta,

Agarwal,

Gupta

et al. 2023

Energies

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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.

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Section: Literature Reviewmentioning

confidence: 99%

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

Gupta,

Agarwal,

Gupta

et al. 2023

Energies

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“…Sharing individual data directly might cause security problems not only for individuals but also for national security [51,52]. A federated learning [53] infrastructure for SGs could help establish collaborative energy consumption pattern learning without sharing individual data [54]. In most cases, to protect user privacy and secure power traces, the collected raw data are stored locally in the user system, and the model also should be trained locally to prevent data leakage, while the model's results are encrypted before the exchange.…”

mentioning

confidence: 99%

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Hachache,

Labrahmi,

Grilo

et al. 2024

Energies

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Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

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Probabilistic load forecasting based on quantile regression parallel CNN and BiGRU networks

Lu,

Wang,

Huang

et al. 2024

Appl Intell

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FedForecast: A federated learning framework for short-term probabilistic individual load forecasting in smart grid

Cited by 8 publications

References 35 publications

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

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

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Probabilistic load forecasting based on quantile regression parallel CNN and BiGRU networks

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