Electric vehicle charging load forecasting based on federal learning

Wu, Yi; Shen, Zhufu; Tian, Yingjie; Cai, Zhengyu; Li, Fan

doi:10.1117/12.2634646

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…These advanced methodologies address the challenge of safeguarding privacy in scenarios involving a substantial volume of data required for power load model training. To overcome these challenges, [50] devises the FRF-CNN model, a hybrid solution that amalgamates federated learning, random forest, and convolutional neural network approaches.…”

Section: Federated Learningmentioning

confidence: 99%

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

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With the rapid development of wireless communication technology, positioning technology, and modern smart devices, Internet of Vehicles (IoVs) smart vehicles have brought great convenience to human production and life. Meanwhile, privacy and security issues are becoming extremely serious, with serious consequences if sensitive data such as vehicle location and trip patterns are leaked. This paper focuses on the demands for vehicular network security, especially privacy protection and existing privacy-protection techniques, including common cryptography methods and cryptography-based advanced technologies. At the same time, this paper also analyzes the advantages and challenges of these technologies in protecting privacy and network security in the Internet of Vehicles, such as the challenges of computational resource requirements and security efficiency in the implementation process, as well as the complexity of realizing effective privacy protection in the interactions among different entities. Finally, this paper envisions the development of privacy-preserving application scenarios and the prospects for crypotography-based privacy-preserving technologies.

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Section: Federated Learningmentioning

confidence: 99%

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Li,

Bi,

Jiang

et al. 2024

Electronics

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“…In [99], the authors combine FL with random forests and CNN for power load prediction. To save communication overhead, the work in [100] adopts a clustering-based approach on the CS side to reduce the data set dimension.…”

Section: Service Providingmentioning

confidence: 99%

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Zhu

Yin

et al. 2023

IEEE Trans. Intell. Transport. Syst.

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“…Machine learning methods generally include XGBoost , random forest [21], fuzzy logic [22], support vector machine (SVM) [23], artificial neural network (ANN) [24] and deep neural network (deep neural network) network, DNN) [25] and other datadriven machine learning methods [26] and so on. Literature [27] proposes an electric vehicle charging load prediction model based on the fusion of Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM); Literature [28] uses the Prophet algorithm The field of load forecasting was introduced and combined with the XGBoost algorithm to improve the accuracy of Prophet load forecasting; the literature [29] proposed a power system load forecasting method that combines fuzzy clustering and random forest regression algorithms, using rough sets to construct compensation rules , to correct and compensate the prediction results; the literature [30] combines high correlation factor data and uses the random forest method to predict monthly electricity consumption. Literature [31] proposed a method based on empirical wavelet transform and random forest to solve the problem of strong randomness and low prediction accuracy of short-term electric load.…”

Section: Introductionmentioning

confidence: 99%

Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network

Zhang,

Dong,

et al. 2024

Preprint

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Oil and gas fields have a large amount of distributed new energy. In order to improve the utilization rate of new energy and respond to the dispatching needs of China's State Grid, it is necessary to study the use of ultra-short-term load forecasting algorithms to improve the load forecasting accuracy of oil and gas fields and support the coordinated interaction of source, grid and load in the integrated energy system of oil and gas fields. This paper proposes an ultra-short-term load forecasting algorithm based on a hybrid neural network called Convolutional-Bidirectional Long Short Term Memory-Skip (CNN-BiLSTM-Skip). Using the operating load data of an oil and gas field in Northeast China as a data set, we first constructed a cooling, heating and power system architecture model with wind turbines, photovoltaics, power grids and natural gas as “source and grid loads”; Secondly, we used an improved hybrid multi-time scale algorithm and unit A prediction model was constructed based on the operating load data, and the prediction results of the nonlinear part and linear part of the model were output and integrated to obtain the final prediction result; Finally, the prediction error evaluation index of the algorithm proposed in this article was compared with algorithms such as BP, LSTM, and CNN-LSTM. The results show that the algorithm proposed in this article has stronger robustness and higher accuracy. The proposed CNN-BiLSTM-SKIP algorithm improves the prediction accuracy. Compared with the BP neural network algorithm, the MAPE evaluation index has an average accuracy increase of 3.78%, compared with the LSTM prediction algorithm, the accuracy has increased by 1.63% on average, and compared with the CNN-LSTM prediction algorithm, the accuracy has increased by 0.74% on average; and the proposed prediction algorithm is compared with the BP neural network algorithm, LSTM prediction algorithm and CNN-LSTM algorithm, the RMSE and MAE evaluation index values are both the smallest, which can support the collaborative interaction of oil and gas field source, network and load and realize the planning and dispatching needs.

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Electric vehicle charging load forecasting based on federal learning

Cited by 4 publications

References 13 publications

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Methods and Challenges of Cryptography-Based Privacy-Protection Algorithms for Vehicular Networks

Enhanced Federated Learning for Edge Data Security in Intelligent Transportation Systems

Research on ultra-short-term load forecasting method of oil and gas field integrated energy system based on hybrid neural network

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