The DOI matches its Crossref record.
A neural network short term load forecasting model for the Greek power system
Abstract
This paper presents the development of an artificial neural network (ANN) based short-term load forecasting model for the Energy Control Center of the Greek Public Power Corporation (PPC). The model can forecast daily load profiles with a lead time of one to seven days. Attention was paid for the accurate modeling of holidays. Experiences gained during the development of the model regarding the selection of the input variables, the ANN structure, and the training data set are described in the paper. The results indicate that the load forecasting model developed provides accurate forecasts.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Neural network load forecasting with weather ensemble predictions
Taylor, J. W., & Buizza, R. (2002) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.800906 · Cited by 459
Short-Term Load Forecasting: Similar Day-Based Wavelet Neural Networks
Chen, Y., Luh, P. B., Guan, C., Zhao, Y., Michel, L. D., Coolbeth, M. A., Friedland, P. B., & Rourke, S. J. (2010) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2030426 · Cited by 444
Machine learning for microalgae detection and utilization
Ning, H., Li, R., & Zhou, T. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.947394 · Cited by 83
Metadata
- Authors
- Bakirtzls, A. G., Petridls, V., Klartzis, S. J., Alexladls, M. C., & Malssls, A. H.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1996-05
- Volume
- 11
- Issue
- 2
- Pages
- 858-863
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W1899402344
- Cited by
- 373
- References
- 0
- FWCI
- 8.25
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: A neural network short term load forecasting model for the Greek power system. https://app.cochat.ai/papers/10.1109/59.496166
- The paper
- Bakirtzls, A. G., Petridls, V., Klartzis, S. J., Alexladls, M. C., & Malssls, A. H. (1996). A neural network short term load forecasting model for the Greek power system. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.496166
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.
