Abstract
Accurate prediction of power load plays an important role in the optimal scheduling of resources. However, the lack of power data in the traditional automatic acquisition system inevitably affects the subsequent data analysis. With the help of on-site real-time monitoring, the integrity of data collection can be ensured. In this paper, a load forecasting model based on the fusion of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) is proposed. Through training the historical data collected by the on-duty robot, a complete network model is constructed. The network extracts the effective sequence features of the input data through CNN network, and gets the load prediction results through LSTM network. The experimental results show that the fusion network of CNN and LSTM obtains higher prediction accuracy than present algorithms.
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Acknowledgement
This work was supported by Science and Technology Project of State Grid Corporation Headquarters: “Research and Application Verification on Intelligent Cloud Robot for Distribution Station” (5700-202018266A-0-0-00).
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Zhuang, Y., Chen, M., Pan, F., Feng, L., Liang, Q. (2021). Research on Short Term Power Load Forecasting Combining CNN and LSTM Networks. In: Liu, XJ., Nie, Z., Yu, J., Xie, F., Song, R. (eds) Intelligent Robotics and Applications. ICIRA 2021. Lecture Notes in Computer Science(), vol 13014. Springer, Cham. https://doi.org/10.1007/978-3-030-89098-8_59
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DOI: https://doi.org/10.1007/978-3-030-89098-8_59
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