Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction
Author:
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence,Software
Link
https://link.springer.com/content/pdf/10.1007/s00521-021-06619-x.pdf
Reference45 articles.
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2. Duman S, Li J, Wu L, Guvenc U (2020) Optimal power flow with stochastic wind power and FACTS devices: a modified hybrid PSOGSA with chaotic maps approach. Neural Comput Appl 32(12):8463–8492. https://doi.org/10.1007/s00521-019-04338-y
3. Hao M, Zhang W, Wang Y, Lu G, Wang F, Vasilakos AV (2020) Fine-grained powercap allocation for power-constrained systems based on multi-objective machine learning. IEEE Trans Parallel Distrib Syst. https://doi.org/10.1109/TPDS.2020.3045983
4. Zhu R, Liao W, Wang Y (2020) Short-term prediction for wind power based on temporal convolutional network. Energy Rep 6:424–429. https://doi.org/10.1016/j.egyr.2020.11.219
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