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3rd International Conference on energy, Power and Electrical technology, ICEPET 2024

作者： Xu, Xinyu Zhou, Zhenliu Zhao, Huaxin Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9798350352658

In order to improve the accuracy of short-term power load forecasting and provide a stronger guarantee for the stable operation of the power system, this paper proposes a short-term power load forecasting method TCN-LSTM, which combines time convolution network ( TCN ) and long short-term memory ( LSTM ). Firstly, the time series features in the original load data are extracted by using the characteristics of TCN. Then, the extracted temporal features and non-temporal features are input into the LSTM model to train the model;finally, the trained model is used to predict the short-term power load data. Through the simulation experiment of the load data of a certain area in the south, the TCN-LSTM combined model is compared with the single model. It is found that the prediction accuracy of the TCN-LSTM model is higher, indicating that the model has better short-term power load forecasting *** experimental simulation comparison shows that the prediction accuracy of the combined model proposed in this paper is 0.66 % higher than that of the basic model. © 2024 IEEE.

关键词： Prediction models

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Short-term wind power load forecasting based on ISSA-CNN-BiLSTM 3

Short-term wind power load forecasting based on ISSA-CNN-BiL...

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3rd International Conference on energy, Power and Electrical technology, ICEPET 2024

作者： Liu, Shaoxiong Zhou, Zhenliu Zhao, Huaxin Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9798350352658

Accurate wind power prediction is of great significance in the balance of electricity supply and demand. Aiming at the problems of poor convergence and easy to fall into local extremes of the power prediction model using the traditional sparrow algorithm (SSA) to optimise the parameters of the bidirectional long- and short-term memory neural network (CNN-BiLSTM), this paper adopts the refractive inverse learning strategy, the positive cosine strategy and the Cauchy variation strategy to improve the sparrow search algorithm, and a better convergence is obtained. An improved sparrow search algorithm (ISSA) with stronger convergence is obtained. The ISSA algorithm is used to optimise the bi-directional long and short-term memory neural network, and the ISSA-CNN-BiLSTM neural network prediction model is established. This paper takes the data of a wind farm in Inner Mongolia as the research object, and uses SSA-CNN-BiLSTM algorithm,CNN-BiLSTM algorithm and ISSA-CNN-BiLSTM algorithm to simulate and test the measured historical data of this wind farm, respectively. The experimental simulation comparison shows that the combined model proposed in this paper has a 7.6% improvement in prediction accuracy over its base model. © 2024 IEEE.

关键词： Wind forecasting

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A Construction Method for Grade Protection System Based on STRIDE Threat Modeling

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Zhao, Dan Zhou, Zhenliu Wang, Hongjiang School of Information Shenyang Institute of Engineering Liaoning Shenyang110136 China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China

In recent years, against the backdrop of the national high attention to cybersecurity, the implementation of grade protection systems has been vigorously carried out in various industries within the internet+ environment. However, the construction of grade protection systems has gradually revealed several issues: network product providers have a weak security awareness and lack of secure development experiences, leading to systems often failing to meet their grade protection requirements;Grade protection evaluation is complex and tedious, consuming a significant amount of time and labor costs. To address the issues above, this paper proposes a construction method for grade protection systems based on STRIDE threat modeling: by establishing the correspondence between threats and grade protection requirements, threats are eliminated during the system development process while implementing the content of grade protection requirements;through the security verification of threat elimination, materials for grade protection evaluation are accumulated, and work reuse is leveraged to alleviate the workload of grade protection evaluation. Implementing this method can effectively promote the construction of grade protection systems. © 2024 Dan Zhao.

关键词： Cybersecurity

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Short-term power load forecasting based on TCN-LSTM model

Short-term power load forecasting based on TCN-LSTM model

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energy, Power and Electrical technology (ICEPET), International Conference on

作者： Xinyu Xu Zhenliu Zhou Huaxin Zhao Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

In order to improve the accuracy of short-term power load forecasting and provide a stronger guarantee for the stable operation of the power system, this paper proposes a short-term power load forecasting method TCN-LSTM, which combines time convolution network ( TCN ) and long short-term memory ( LSTM ). Firstly, the time series features in the original load data are extracted by using the characteristics of TCN. Then, the extracted temporal features and non-temporal features are input into the LSTM model to train the model ; finally, the trained model is used to predict the short-term power load data. Through the simulation experiment of the load data of a certain area in the south, the TCN-LSTM combined model is compared with the single model. It is found that the prediction accuracy of the TCN-LSTM model is higher, indicating that the model has better short-term power load forecasting *** experimental simulation comparison shows that the prediction accuracy of the combined model proposed in this paper is 0.66 % higher than that of the basic model.

关键词： Accuracy Load forecasting Convolution Time series analysis Predictive models Feature extraction Data models

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Short-term wind power load forecasting based on ISSA-CNN-BiLSTM

Short-term wind power load forecasting based on ISSA-CNN-BiL...

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energy, Power and Electrical technology (ICEPET), International Conference on

作者： Shaoxiong Liu Zhenliu Zhou Huaxin Zhao Electrical Engineering College of Shenyang Institute of Engineering Shenyang China Shenyang Key Laboratory of Energy Internet Intelligent Perception and Security Technology Shenyang China Information College of Shenyang Institute of Engineering Shenyang China

ISBN: (数字)9798350352658

ISBN: (纸本)9798350352665

关键词： Accuracy Neural networks Predictive models Wind power generation Wind farms Prediction algorithms Data models

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