Short-term power load forecasting based on deep learning

Yangyu Xu; Yaner Feng

doi:10.1117/12.2645056

30 August 2022 Short-term power load forecasting based on deep learning

Yangyu Xu, Yaner Feng

Proceedings Volume 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022); 123092Y (2022) https://doi.org/10.1117/12.2645056
Event: International Conference on Advanced Manufacturing Technology and Manufacturing System (ICAMTMS 2022), 2022, Shijiazhuang, China

Abstract

In the process of our daily use of electricity, power load forecasting is very important. Short-term power load forecasting can effectively manage electric energy. In order to improve the intelligence of power grid, this paper studies the analysis and prediction methods of power load based on deep learning. Short-term power load forecasting can accurately predict the load and electricity consumption of a certain area, and provide reference for the operation of the power system. In daily life, our power load data are interfered by many factors, so we need to fully find out the temporal characteristics of power load data, so as to improve the accuracy of our power load forecasting. In this paper, a convolutional neural network-long-term and short-term memory neural network (CNN-LSTM) neural network prediction model based on convolution neural network and long-term and short-term memory neural network is proposed. The data characteristics are extracted by convolutional neural network (CNN), and then the accurate prediction of power load is completed by using the unique memory and prediction ability of long-term and short-term memory neural network (LSTM) neural network. The experimental results show that compared with the single LSTM neural.

Citation Download Citation

Yangyu Xu and Yaner Feng "Short-term power load forecasting based on deep learning", Proc. SPIE 12309, International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2022), 123092Y (30 August 2022); https://doi.org/10.1117/12.2645056

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KEYWORDS

Neural networks

Data modeling

Convolutional neural networks

Data processing

Feature extraction

Performance modeling

Convolution

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