Abstract
Short-term power load prediction is not only a critical part of power system dispatching but also an essential task for power marketing, grid planning, and other management departments; deep learning is an artificial intelligence method that has gained extensive attention in recent years; this paper selected three models with the most typical signs of deep learning recurrent neural networks and studied their model performance for short-term power load predictions, including power load data preprocessing, feature selection of prediction models, determination of model parameters, and comparison of prediction characteristics between shallow and deep networks, to explore and prove the applicability of deep learning neural networks in short-term power load predictions, ultimately improving the accuracy of short-term load predictions.
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References
Zhang, L., Wang, H., Liu, W., Liu, M.: Short term load forecasting based on high dimensional data and deep learning. Sci. Technol. Bull. 37(03), 55–59+66 (2021)
Yang, Z., Ding, S., Ye, M., Li, J., Xue, S., Wu, H.: Short term load forecasting model based on variational mode decomposition and deep learning. Electrical measurement and instrumentation
Li, X., Lian, D., Jing, C., Li, R., Xu, Z., Yue, S.: Short term power load forecasting based on wekpca and deep learning. Power Inf. Commun. Technol. 18(10), 34–41 (2020)
Yao, D., Wu, Y., Lei, L., Yan, S., Wu, W., Hong, D.: Short term load forecasting based on deep learning. Foreign Electron. Measure. Technol. 39(01), 44–48 (2020)
Ma, T., Wang, C., Peng, L., Guo, X., Fu, M.: Short term Load Forecasting Considering Demand Response and deep structure multi task learning. Electric. Measure. Instrum. 56(16), 50–60 (2019)
Chen, Z., Sun, L.: Short term power load forecasting method based on deep learning LSTM network. Electron. Technol. 47(01), 39–41 (2018)
Liang, C., Zhen, W., Gang, W.: Application of LSTM network in short-term power load forecasting under deep learning framework. Power Inf. Commun. Technol. 15(05), 8–11 (2017)
Dong, H., Cheng, P., Li, L.: Application of deep learning algorithm in short-term load forecasting of power system. Electric. Times (02), 82–84 (2017)
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2019 Guangxi Basic Research Ability Improvement Project for Young and Middle-Aged University Teachers (2019KY0640).
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Yin, L., Mo, F., Wu, Q., Xiong, S. (2022). Research on Short-Term Power Load Prediction Based on Deep Learning. In: Liu, Q., Liu, X., Chen, B., Zhang, Y., Peng, J. (eds) Proceedings of the 11th International Conference on Computer Engineering and Networks. Lecture Notes in Electrical Engineering, vol 808. Springer, Singapore. https://doi.org/10.1007/978-981-16-6554-7_17
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DOI: https://doi.org/10.1007/978-981-16-6554-7_17
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