Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

Zhang, Ge; Zhu, Songyang; Bai, Xiaoqing

doi:10.3390/su141912843

Cited by 25 publications

(3 citation statements)

References 39 publications

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“…In this way the computationally constrained devices may get some relaxation in the training phase. On the similar path, authors in [9] utilizes CNN-Attention-LSTM model, leveraging the federated learning methodology with the aim to optimize predictive accuracy within integrated energy systems. Furthermore, authors also implemented various federated learning algorithms, including FedAvg, FedAdagrad, FedYogi, and FedAdam, in their investigation.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, the proposed work performed an experiment 3 and developed a ML model using federated learning approach, which would be able to forecast the GHI value. The predicted GHI value would play a crucial role in determining the total energy to be generated by using Equation (9). In this experiment, each solar energy generation station will comprise a weather station which will work as a client to train a model in FL approach as represented in Figure 7.…”

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confidence: 99%

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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%

mentioning

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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“…To mitigate privacy leakage caused by direct data sharing, FL has been applied to many applications in the smart grid [15][16][17]. Hudson et al [18] proposed a deep recurrent network for nonintrusive load monitoring via FL.…”

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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Fog-based Federated Time Series Forecasting for IoT Data

Sharma,

Kaur

2024

J Netw Syst Manage

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Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model

Cited by 25 publications

References 39 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

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

Fog-based Federated Time Series Forecasting for IoT Data

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