Energy load forecasting model based on deep neural networks for smart grids
Author:
Funder
National Research Foundation of Korea
Publisher
Springer Science and Business Media LLC
Subject
Strategy and Management,Safety, Risk, Reliability and Quality
Link
http://link.springer.com/content/pdf/10.1007/s13198-019-00884-9.pdf
Reference25 articles.
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3. Anvari Moghaddam A, Seifi AR (2011) Study of forecasting renewable energies in smart grids using linear predictive filters and neural networks. IET Renew Power Gener 5(6):470–480
4. Çavdar IH, Faryad V (2018) New design of a supervised energy disaggregation model based on the deep neural network for a smart grid. Energies 11(1):213
5. Chen K, Chen K, Wang Q, He Z, Hu J, He J (2018) Short-term load forecasting with deep residual networks. IEEE Trans Smart Grid. https://doi.org/10.1109/tsg.2018.2844307
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