Abstract
Power load data has obvious timing dependence. Aiming at the time-dependent characteristics of power load, an adaptive depth long-term and short-term memory network model is proposed to predict power load. The model can extract sequential dependencies of load sequences effectively through deep memory networks. In addition, the input adaptive measurement of the model can solve the problem of amplitude change and trend determination, and avoid over-fitting of the network. The experimental results show that the model is superior to BP neural network, autoregressive model, grey system, limit learning machine model and K-nearest neighbor model. Adaptive depth LSTM network provides a new effective method for power load forecasting.
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Wu, J., Zhang, P., Zheng, Z., Xia, M. (2019). Power Load Forecasting Based on Adaptive Deep Long Short-Term Memory Network. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11632. Springer, Cham. https://doi.org/10.1007/978-3-030-24274-9_40
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DOI: https://doi.org/10.1007/978-3-030-24274-9_40
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