The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Day-ahead electricity price forecasting via the application of artificial neural network based models
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Citations
Cites
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
Cited by
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
Wang, D., Luo, H., Grunder, O., Lin, Y., & Guo, H. (2017) · Applied Energy · doi:10.1016/j.apenergy.2016.12.134 · Cited by 312
Time Series-Based GHG Emissions Prediction for Smart Homes
Riekstin, A. C., Langevin, A., Dandres, T., Gagnon, G., & Cheriet, M. (2020) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2018.2886164 · Cited by 41
Metadata
- Authors
- Panapakidis, I. P., & Dagoumas, A. S.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-06
- Volume
- 172
- Pages
- 132-151
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2315598830
- Cited by
- 317
- References
- 1
- FWCI
- 17.07
- Type
- journal article
- Language
- en
- Open access
- closed
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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- CoChat. (2026). Citation check: Day-ahead electricity price forecasting via the application of artificial neural network based models. https://app.cochat.ai/papers/10.1016/j.apenergy.2016.03.089
- The paper
- Panapakidis, I. P., & Dagoumas, A. S. (2016). Day-ahead electricity price forecasting via the application of artificial neural network based models. Applied Energy. https://doi.org/10.1016/j.apenergy.2016.03.089
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.
