Machine learning with parallel neural networks for analyzing and forecasting electricity demand-Reference-Cited by-同舟云学术

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Machine learning with parallel neural networks for analyzing and forecasting electricity demand

Published:2019-12-18 Issue:2 Volume:56 Page:569-597
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Chen Yi-Ting,Sun Edward W.,Lin Yi-Bing

Funder

InfoTech Frankfurt, Germany

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

http://link.springer.com/content/pdf/10.1007/s10614-019-09960-5.pdf

Reference31 articles.

1. Abedini, O., Zareinejad, M., Doranehgard, M., Fathi, G., & Ghadimi, N. (2019). Optimal offering and bidding strategies of renewable energy based large consumer using a novel hybrid robust-stochastic approach. Journal of Cleaner Production, 215(1), 878–889.

2. Baesens, B., Setiono, R., Mues, C., & Vanthienen, J. (2003). Using neural network rule extraction and decision tables for credit-risk evaluation. Management Science, 49, 312–329.

3. Baucells, M., & Borgonovo, E. (2013). Invariant probabilistic sensitivity analysis. Management Science, 59, 2536–2549.

4. Cai, Y., Judd, K. L., Thain, G., & Wright, S. J. (2015). Solving dynamic programming problems on a computational grid. Computational Economics, 45(2), 261–284.

5. Chen, Y., Luh, P. B., Guan, C., Zhao, Y., Michel, L. D., Coolbeth, M. A., et al. (2010). Short-term load forecasting: Similar day-based wavelet neural networks. IEEE Transactions on Power Systems, 25, 322–330.

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