Aggregate Load Forecasting in Residential Smart Grids Using Deep Learning Model
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-45170-6_2
Reference15 articles.
1. Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst. Appl. 140, 112896 (2020)
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3. Feng, C., Zhang, J.: Assessment of aggregation strategies for machine-learning based short-term load forecasting. Electr. Power Syst. Res. 184, 106304 (2020)
4. Gu, Y., Chen, Q., Liu, K., Xie, L., Kang, C.: GAN-based model for residential load generation considering typical consumption patterns. In: 2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5 (2019)
5. Hahnloser, R.H., Sarpeshkar, R., Mahowald, M.A., Douglas, R.J., Seung, H.S.: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit. Nature 405(6789), 947–951 (2000)
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