Abstract
The accuracy of power load forecasting is sensitive to various characteristic factors. For the massive historical load data, how to mine the correlation between different attributes in the characteristic data set is the key to improve the accuracy of load forecasting. In this paper, a load forecasting method based on Bi-LSTM network considering data feature analysis is proposed. Firstly, the attribute correlation feature analysis of massive historical load data is realized by using random forest algorithm, and the features with strong correlation with load are selected as the input of the model; Secondly, Bi-LSTM network with good data fitting accuracy is used for load forecasting modeling. Finally, the actual load data of a city is used for simulation analysis. The simulation results show that this method has more advantages in accuracy than traditional forecasting methods, which verifies the effectiveness and practicability of this method.
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Ding, Z., Wu, Y., Jia, P., Li, J., Zhou, S. (2022). Research on Load Forecasting Method Considering Data Feature Analysis Based on Bi-LSTM Network. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_20
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DOI: https://doi.org/10.1007/978-981-19-0852-1_20
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