A Differential Privacy-enhanced Federated Learning Method for Short-Term Household Load Forecasting in Smart Grid

Zhang, Xiaochen; Xiao, Wendan; Shuai, Lisha; Luo, Jingtang; Yao, Shiying; Zhang, Min

doi:10.1109/iccc54389.2021.9674514

Cited by 11 publications

(5 citation statements)

References 13 publications

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“…Data heterogeneity [4], [17], [18], [25], [27], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44] x x [45] x x [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60] x [61] x x x [28], [62], [63] x x [64] x x [7], [20] x x x x [8], [9], [19], [65], [66], [67], [68], [69], [70], [71],…”

Section: Model Generalization Abilitymentioning

confidence: 99%

“…Demand forecasting [8], [9], [19], [25], [26], [27], [30], [31], [32], [33], [34], [40], [51], [55], [63], [65], [66], [67], [69], [70], [73], [74], [77], [79], [81], [90], [91], [92] Achieving Generation forecasting [7], [35], [38],…”

Section: Forecastingmentioning

confidence: 99%

“…For instance, the study [25] introduced the federated learning process of how sensitive data like the consumption habits of customers will be protected. FL just transmits the [65]. For a specific scenario, a suitable aggregation algorithm needs to be selected.…”

Section: B Opportunities and Challengesmentioning

confidence: 99%

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Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review

Zheng,

Sumper,

Aragüés-Peñalba

et al. 2024

IEEE Access

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Digitalization has enabled the potential for artificial intelligence techniques to lead the power system to a sustainable transition by extracting the data generated by widely deployed edge devices, including advanced sensing and metering. Due to the increasing concerns about data privacy, federated learning has attracted much attention and is emerging as an innovative application for machine learning solutions in the power and energy sector. This paper presents a holistic analysis of federated learning applications in the energy sector, ranging from applications in generation, microgrids, and distribution systems to the energy market and cyber security. The following federated learning-based services for energy sectors are analyzed: non-intrusive load monitoring, fault detection, energy theft detection, demand forecasting, generation forecasting, energy management systems, voltage control, anomaly detection, and energy trading. The identification and classification of the data-driven methods are conducted in collaboration with federated learning implemented in these services. Furthermore, the interrelation is mapped between the categories of machine learning, data-driven techniques, the application domain, and application services. Finally, the future opportunities and challenges of applying federated learning in the energy sector will be discussed.

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Section: Model Generalization Abilitymentioning

confidence: 99%

Section: Forecastingmentioning

confidence: 99%

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Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review

Zheng,

Sumper,

Aragüés-Peñalba

et al. 2024

IEEE Access

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“…Thus it concludes by highlighting the effectiveness of the proposed approach and demonstrates significant improvements in mitigating Byzantine threats compared to traditional FedSGD models. Furthermore, authors in [13] developed Differential Privacyenhanced Federated Learning (DPEFL) for the development of LSTM load forecasting models using data distributed across multiple consumer households. The effectiveness of the DPEFL approach was evaluated through simulations conducted using real-world household data from the Pecan Street dataset, which contains data from households in Texas, USA.…”

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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“…Additionally, Wang et al [16] analyzed the consumer's characteristic dataset, leveraged privacy-enhanced PCA to diminish the features, and applied it to the ANN for target attribute identification. Zhao et al [17] applied differential privacy into FL to enhance the security of local-level LSTM that predicts the energy consumption of individual households' datasets. Similarly, privacy-preserving FL was recently studied in energy grid environments, most of them to achieve the secure aggregation of local information at the global level [18,19].…”

Section: Related Workmentioning

confidence: 99%

Towards Convergence in Federated Learning via Non-IID Analysis in a Distributed Solar Energy Grid

Lee

2023

Electronics

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Federated Learning (FL) is an effective framework for a distributed system that constructs a powerful global deep learning model, which diminishes the local bias and accommodates the successful aggregation of locally trained models with heterogeneous datasets. However, when local datasets have the non-IID attribute, the optimization metric tends to diverge or show unstable convergence in the trajectory space. This paper delves into building a global model for the distributed Smart Grid environment, with regionally cumulated three solar energy datasets from January 2017 to August 2021 in a decentralized power grid in South Korea via FL. This distributed energy network involves local properties and physical distance between the regions, which raises a fundamental question of “Will time-serially curated non-IID local features be effective in constructing a global regression model?”. This paper probes this question by leveraging FL and conducts the theoretically viable non-IID case-by-case convergence analysis, providing the interpretation of the embedded temporal non-IID features and application on real-world data. Moreover, most of the FL studies predetermine the global update period, which lacks applicability when adapting FL in actual practice. As FL is a cumulative-basis structure, the update term is a crucial factor that needs to be carefully selected. This paper articulates this problem and explores the effective update period via multiple experiments on the 4.5 years of solar energy dataset, and to the best of my knowledge, this is the first literature that presents the optimal update period in the FL regression in an energy domain.

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A Differential Privacy-enhanced Federated Learning Method for Short-Term Household Load Forecasting in Smart Grid

Cited by 11 publications

References 13 publications

Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review

Advancing Power System Services With Privacy-Preserving Federated Learning Techniques: A Review

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

Towards Convergence in Federated Learning via Non-IID Analysis in a Distributed Solar Energy Grid

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