Privacy-preserving federated learning for residential short-term load forecasting

Fernández, Joaquín Delgado; S, Menci; Lee, Chul Min; Rieger, Alexander; Fridgen, Gilbert

doi:10.1016/j.apenergy.2022.119915

Cited by 52 publications

(11 citation statements)

References 41 publications

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“…Through the three elements of user load, the peak value of user load and the time period of occurrence can be predicted, so as to provide reference for power grid planning. In this paper, the installed capacity of 1 125 10 kV nonresidents and 130 return lines for residential users in Pudong area was statistically analyzed, and the maximum load was reflected by the demand coefficient to provide data support for the subsequent load prediction [5][6].…”

Section: Load Analysis Of User Installed Capacitymentioning

confidence: 99%

Load forecasting method for power users based on capacity constraints and historical data characteristics of connected equipment

Zhang,

Wang,

Wang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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The load of power users is varied and uncertain, and the behavior of power users is affected by many factors, which makes the load prediction difficult. Therefore, a load forecasting method for power users based on capacity constraints and historical data characteristics is proposed in this paper. Analyze the load of the installed capacity of the user and the installed capacity of the equipment in the distribution network. Under the capacity constraint of the installed equipment, the abnormal historical data is searched and corrected. Based on this, empirical mode decomposition (EMD) is used to forecast the load of power users. The experimental results show that the predicted value of power user side load obtained by the proposed method is basically consistent with the real value, and can effectively shorten the time of power user side load prediction.

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Section: Load Analysis Of User Installed Capacitymentioning

confidence: 99%

Load forecasting method for power users based on capacity constraints and historical data characteristics of connected equipment

Zhang,

Wang,

Wang

et al. 2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…In this regard, various strategies have been proposed to secure the model updates while transmission. The author in [11] proposed FL based short term load forecasting (SLTF) by employing differential privacy and secure aggregation to provide additional security to entire federated learning setup. The authors conducted an analysis of different neural network (NN) architectures and evaluated various scenarios using real-world historical data to assess the performance and privacy implications of FL for STLF.…”

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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“…However, they also considered the horizontal FL, which cannot be extended to the vertical scenario considered in the paper. Te work most similar to ours is [24], where vertical FL XGBoost is applied to power consumption forecast. Te diference is that the study [24] only discusses how to use an encryption scheme to enhance the training phase, but we give a complete vertical FL framework based on a practical data processing procedure.…”

Section: Federated Learningmentioning

confidence: 99%

Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning

Mao

Huang

et al. 2023

Journal of Electrical and Computer Engineering

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Power load forecasting (PLF) has a positive impact on the stability of power systems and can reduce the cost of power generation enterprises. To improve the forecasting accuracy, more information besides load data is necessary. In recent years, a novel privacy-preserving paradigm vertical federated learning (FL) has been applied to PLF to improve forecasting accuracy while keeping different organizations’ data locally. However, two problems are still not well solved in vertical FL. The first problem is a lack of a full data-processing procedure, and the second is a lack of enhanced privacy protection for data processing. To address it, according to the procedure in a practical scenario, we propose a vertical FL XGBoost-based PLF, where multiparty secure computation is used to enhance the privacy protection of FL. Concretely, we design a full data-processing PLF, including data cleaning, private set intersection, feature selection, federated XGBoost training, and inference. Furthermore, we further use RSA encryption in the private set intersection and Paillier homomorphic encryption in the training and inference phases. To validate the proposed method, we conducted experiments to compare centralized learning and vertical FL on several real-world datasets. The proposed method can also be directly applied to other practical vertical FL tasks.

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Privacy-preserving federated learning for residential short-term load forecasting

Cited by 52 publications

References 41 publications

Load forecasting method for power users based on capacity constraints and historical data characteristics of connected equipment

Load forecasting method for power users based on capacity constraints and historical data characteristics of connected equipment

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

Full Data-Processing Power Load Forecasting Based on Vertical Federated Learning

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