Abstract
With the rapid development of smart grids, significant research has been devoted to the methodologies for short-term load forecasting (STLF) due to its significance in forecasting demand on electric power. In this paper an enhanced LSTM model is proposed to upgrade the state-of-the-art LSTM network by exploiting the long periodic information of load, which is missed by the standard LSTM model due to its constraint on input length. In order to distill information from long load sequence and keep the input sequence short enough for LSTM, the long load sequence is reshaped into two-dimension matrix whose dimension accords to the periodicity of load. Accordingly, two LSTM networks are paralleled: one takes the rows as input to extract the temporal pattern of load in short time, while the other one takes the columns as input to distill the periodicity information. A multi-layer perception combines the two outputs for more accurate load forecasting. This model can exploit more information from much longer load sequence with only linear growth in complexity, and the experiment results verify its considerable improvement in accuracy over the standard LSTM model.
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Acknowledgment
The work of Q. Zhou and Q. Lv is partly supported by project with Grant No. SGGSKY00FJJS1900241.
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Guo, J., Peng, Y., Zhou, Q., Lv, Q. (2020). Enhanced LSTM Model for Short-Term Load Forecasting in Smart Grids. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_52
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DOI: https://doi.org/10.1007/978-3-030-48513-9_52
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