An integrated federated learning algorithm for short-term load forecasting

Yang, Yang; Wang, Zijin; Zhao, Shangrui; Wu, Jinran

doi:10.1016/j.epsr.2022.108830

Cited by 34 publications

(5 citation statements)

References 25 publications

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“…To tackle this concern, the authors integrated variational mode decomposition (VMD), the federated k-means clustering algorithm (FK), and SecureBoost into a unified algorithm, named VMD-FK-SecureBoost. Yang et al (2023).…”

Section: Related Workmentioning

confidence: 99%

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Jain,

Gupta

2024

Front. Energy Res.

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As the modern power system continues to grow in size, complexity, and uncertainty, traditional methods may occasionally prove insufficient in addressing the associated challenges. The improper location of distributed generation varies the voltage profile, increases losses and compromises network capacity. Machine learning algorithms predict accurate site positions, and network reconfiguration improves the capacity of the power system. The proposed algorithm is a hybrid of machine learning and deep learning algorithms. It cascades Support Vector Machine as the main model and uses Random Forest and Radial Neural Networks as classification algorithms for accurately predicting DG position. The non-linearity characteristics of the DG problem are directly mapped to the proposed algorithms. The proposed algorithm is employed on familiar test setups like the IEEE 33-bus and 69-bus distribution systems using MATLAB R2017 as simulation software. The R-squared (R2) values for all parameters yield a value of 1, while the MAPE values are minimal for the proposed cascaded algorithm in contrast to other algorithms of LSTM, CNN, RNN and DQL.

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Section: Related Workmentioning

confidence: 99%

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Jain,

Gupta

2024

Front. Energy Res.

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“…Nevertheless, a commonly ignored important aspect is the sufficient acknowledgement of the existence of additional triggering factors-such as precipitation, seismic activity, and anthropogenic interventions-that exhibit significant temporal correlations with the deformation process. Incorporating these factors into the predictive models could potentially enhance their performance (Yang et al, 2023b).…”

Section: Introductionmentioning

confidence: 99%

Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit

Cai,

Lan,

Huang

et al. 2024

Front. Earth Sci.

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Landslides, prevalent in mountainous areas, are typically triggered by tectonic movements, climatic changes, and human activities. They pose catastrophic risks, especially when occurring near settlements and infrastructure. Therefore, detecting, monitoring, and predicting landslide deformations is essential for geo-risk mitigation. The mainstream of the previous studies have often focused on deterministic models for immediate landslide prediction. However, in most of them, the aspect of prediction uncertainties are not sufficiently addressed. This paper introduces an innovative probabilistic prediction method using a Variational Autoencoder (VAE) combined with Gated Recurrent Unit (GRU) to forecast landslide deformations from a generative standpoint. Our approach consists of two main elements: firstly, training the VAE-GRU model to maximize the variational lower bound on the likelihood of historical precipitation data; secondly, using the learned approximated posterior distribution to predict imminent deformations from a generative angle. To assess the prediction quality, we use four widely-used metrics: Prediction Interval Coverage Probability (PICP), Prediction Interval Normalized Average Width (PINAW), Coverage Width-Based Criterion (CWC), and Prediction Interval Normalized Root Mean Square Width (PINRW). The results demonstrate that our proposed VAE-GRU framework surpasses traditional state-of-the-art (SOTA) probabilistic deformation prediction algorithms in terms of accuracy and reliability.

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“…However, with the rapid deployment of smart grids, the amount of data on electrical load from smart meters is expanding at an unprecedented rate. Traditional centralized model training is limited by communication and computing capabilities, and it is difficult to collect and store large amounts of data [3]. Meanwhile, data from different participants may have different differences in characteristics, distribution, format, or scale, which will make the convergence speed and accuracy of the prediction model worse [4].…”

Section: Introductionmentioning

confidence: 99%

Distilled one-shot federated learning for heterogeneous residential load forecasting

Guan,

Xie,

Ding

et al. 2024

Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024)

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Load prediction is the foundation of power system operation and analysis. It also plays a decisive role in the safety, stability, and economic operation of the smart grid. Traditional load forecasting methods are too difficult to meet the needs of the modern smart grid. As technology evolves, federated learning (FL) offers a new solution for load forecasting. However, in the training of global model, FL multiple iterations are needed to converge, which largely depends on the repeated transmission of model parameter updates between client and server. When the global model structure is complicated or the local data heterogeneity of each client is serious, more iterations are needed to converge the model. Therefore, we designed a load prediction method based on distillated one-shot federated learning to solve these problems. The proposed method distills the training data through a dataset distillation algorithm and uses the distilled data instead of gradients for transmission, greatly reducing communication costs. In addition, we propose a double-weight self-checking mechanism to avoid each client updating the global model of the server on average and improve the accuracy of load forecasting. The results of the British example show that the prediction performance and privacy of distillated one-shot federated learning are better than those of FedAvg and FedDM. Particularly, our proposed method requires only 0.28 MB of communication consumption during the training process, which is much lower than the above two algorithms.

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An integrated federated learning algorithm for short-term load forecasting

Cited by 34 publications

References 25 publications

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms

Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit

Distilled one-shot federated learning for heterogeneous residential load forecasting

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