Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods-Reference-Cited by-同舟云学术

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Daily Load Curve Forecasting. Comparative Analysis: Conventional vs. Unconventional Methods

Published:2020-08-15 Issue: Volume: Page:3-18
ISSN:2194-5357
Container-title:Soft Computing Applications
language:
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Author:

Barbulescu Constantin,Kilyeni Stefan,Chis Violeta,Craciun Mihaela,Simo Attila

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-51992-6_1

Reference15 articles.

1. Baliyana, A., Gaura, K., Mishra, S.K.: A review of short term load forecasting using artificial neural network models. Procedia Comput. Sci. 48, 121–125 (2015)

2. Muhammad, A., Alam, K.A., Hussain, M.: Application of data mining using artificial neural network: survey. Int. J. Database Theory Appl. 8(1), 245–270 (2015)

3. Dudek, G.: Neural networks for pattern-based short-term load forecasting: a comparative study. Neurocomputing 205, 64–74 (2016)

4. Nuchprayoon, S.: Forecasting of daily load curve on monthly peak day using load research data and harmonics model. In: Proceedings of the 6th IEEE International Conference on Control System, Computing and Engineering, Malaysia, pp. 338–342 (2016)

5. Elgarhy, S.M., Othman, M.M., Taha, A., Hasanien, H.M.: Short term load forecasting us-ing ANN technique. In: Proceedings of the IEEE 19th International Middle East Power Systems Conference (MEPCON), Egypt, pp. 1385–1394 (2017)

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