Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer DOI Creative Commons
Mingxiang Li, Tianyi Zhang,

Haizhu Yang

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(20), P. 5181 - 5181

Published: Oct. 17, 2024

In order to solve the problem of complex coupling characteristics between multivariate load sequences and difficulty in accurate multiple forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission sources, this paper, TCN-FECAM-Informer model is proposed. First, maximum information coefficient (MIC) used correlate loads with weather factors filter appropriate features. Then, effective screened features extracted frequency sequence constructed using frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, processed feature are sent Informer forecasting. Experiments conducted measured data from IRES Arizona State University, experimental results show that TCN FECAM can greatly improve prediction accuracy and, at same time, demonstrate superiority network, dominated by attentional mechanism, compared recurrent neural networks prediction.

Language: Английский

Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning DOI
Nawaf Abdulla, Mehmet Demirci, Suat Özdemır

et al.

Sustainable Energy Grids and Networks, Journal Year: 2024, Volume and Issue: 38, P. 101342 - 101342

Published: March 11, 2024

Language: Английский

Citations

23
A Secure Federated Learning Framework for Residential Short-Term Load Forecasting DOI
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh

et al.

IEEE Transactions on Smart Grid, Journal Year: 2023, Volume and Issue: 15(2), P. 2044 - 2055

Published: July 5, 2023

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as promising privacy-preserving machine learning alternative which enables collaborative of model without exposing private raw short term load forecasting. Despite its virtue, standard FL is still vulnerable an intractable cyber threat known Byzantine attack carried out by faulty and/or malicious clients. Therefore, improve the robustness federated short-term forecasting against threats, we develop state-of-the-art differentially secured FL-based framework that ensures privacy individual smart meter's while protect security models and architecture. Our proposed leverages idea gradient quantization through Sign Stochastic Gradient Descent (SignSGD) algorithm, where clients only transmit 'sign' control centre after local training. As highlight our experiments involving benchmark neural networks with set models, approach mitigates such threats quite effectively thus outperforms conventional FedSGD models.

Language: Английский

Citations

27
Consumers profiling based federated learning approach for energy load forecasting DOI

Atharvan Dogra,

Ashima Anand, Jatin Bedi

et al.

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104815 - 104815

Published: July 26, 2023

Language: Английский

Citations

23
Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction DOI Creative Commons
Nier Wang, Zhanming Li

Frontiers in Energy Research, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 6, 2023

Aiming at the strong non-linear and non-stationary characteristics of power load, a short-term load forecasting method based on bald eagle search (BES) optimization variational mode decomposition (VMD), convolutional bi-directional long memory (CNN-Bi-LSTM) network considering error correction is studied to improve accuracy forecasting. Firstly, loss evaluation criterion established, VMD optimal parameters under are determined BES quality signal. Then, original sequence decomposed into different modal components, corresponding CNN-Bi-LSTM prediction models established for each component. In addition, influence various holiday meteorological factors error, an model mine hidden information contained in reduce inherent model. Finally, proposed applied public dataset provided by utility United States. The results show that this can better track changes effectively

Language: Английский

Citations

21
Federated deep learning for smart city edge-based applications DOI
Youcef Djenouri, Tomasz Michalak, Jerry Chun‐Wei Lin

et al.

Future Generation Computer Systems, Journal Year: 2023, Volume and Issue: 147, P. 350 - 359

Published: May 10, 2023

Language: Английский

Citations

18
Multiscale-integrated deep learning approaches for short-term load forecasting DOI Creative Commons
Yang Yang, Yuchao Gao, Zijin Wang

et al.

International Journal of Machine Learning and Cybernetics, Journal Year: 2024, Volume and Issue: 15(12), P. 6061 - 6076

Published: Aug. 6, 2024

Abstract Accurate short-term load forecasting (STLF) is crucial for the power system. Traditional methods generally used signal decomposition techniques feature extraction. However, these are limited in extrapolation performance, and parameter of modes needs to be preset. To end this, this paper develops a novel STLF algorithm based on multi-scale perspective decomposition. The proposed adopts deep neural network (MscaleDNN) decompose series into low- high-frequency components. Considering outliers series, introduces adaptive rescaled lncosh (ARlncosh) loss fit distribution data improve robustness. Furthermore, attention mechanism (ATTN) extracts correlations between different moments. In two sets from Portugal Australia, model generates competitive results.

Language: Английский

Citations

9
Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems DOI
Nathielle Waldrigues Branco, Mariana Santos Matos Cavalca, Raúl García Ovejero

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: 106(3), P. 3657 - 3670

Published: Jan. 4, 2024

Language: Английский

Citations

7
Graph Convolutional Networks based short-term load forecasting: Leveraging spatial information for improved accuracy DOI
Haris Mansoor, Muhammad Shuzub Gull, Huzaifa Rauf

et al.

Electric Power Systems Research, Journal Year: 2024, Volume and Issue: 230, P. 110263 - 110263

Published: March 5, 2024

Language: Английский

Citations

7
Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit DOI Creative Commons

Wencheng Cai,

Fuan Lan,

Xianhao Huang

et al.

Frontiers in Earth Science, Journal Year: 2024, Volume and Issue: 12

Published: April 24, 2024

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

Language: Английский

Citations

6
FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions DOI

Qumrish Arooj

Energy, Journal Year: 2024, Volume and Issue: 309, P. 133072 - 133072

Published: Sept. 6, 2024

Language: Английский

Citations

6