The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm
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Citations
Cites
Variational Mode Decomposition
Dragomiretskiy, K., & Zosso, D. (2014) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2013.2288675 · Cited by 9,184
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
WU, Z., & HUANG, N. E. (2009) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536909000047 · Cited by 8,715
Firefly algorithm, stochastic test functions and design optimisation
Yang, X. S. (2010) · International Journal of Bio-Inspired Computation · doi:10.1504/ijbic.2010.032124 · Cited by 2,729
Electricity price forecasting: A review of the state-of-the-art with a look into the future
Weron, R. (2014) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2014.08.008 · Cited by 1,646
A comprehensive review of firefly algorithms
Fister, I., Fister, I., Yang, X.-S., & Brest, J. (2013) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2013.06.001 · Cited by 1,308
A review on the forecasting of wind speed and generated power
Lei, M., Shiyan, L., Chuanwen, J., Hongling, L., & Yan, Z. (2009) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2008.02.002 · Cited by 1,156
Firefly algorithm: recent advances and applications
Yang, X. S., & He, X. (2013) · International Journal of Swarm Intelligence · doi:10.1504/ijsi.2013.055801 · Cited by 858
Ben Taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.01.039 · Cited by 734
Firefly Algorithm: Recent Advances and ApplicationsPreprint
Yang, X.-S., & He, X. (2013) · Cited by 708
Wang, S., Zhang, N., Wu, L., & Wang, Y. (2016) · Renewable Energy · doi:10.1016/j.renene.2016.03.103 · Cited by 674
Cited by
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259
Lago, J., De Ridder, F., & De Schutter, B. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.02.069 · Cited by 642
Incentive-based demand response for smart grid with reinforcement learning and deep neural network
Lu, R., & Hong, S. H. (2019) · Applied Energy · doi:10.1016/j.apenergy.2018.12.061 · Cited by 394
Metadata
- Authors
- Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-03
- Volume
- 190
- Pages
- 390-407
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2571217044
- Cited by
- 312
- References
- 29
- FWCI
- 15.55
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017). Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm. Applied Energy. https://doi.org/10.1016/j.apenergy.2016.12.134
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
