The DOI matches its Crossref record.
Neural network based short term load forecasting
Abstract
The artificial neural network (ANN) technique for short-term load forecasting (STLF) has been proposed previously. In order to evaluate ANNs as a viable technique for STLF, one has to evaluate the performance of ANN methodology for practical considerations of STLF problems. The authors make an attempt to address these issues. The results of a study to investigate whether the ANN model is system dependent, and/or case dependent, are presented. Data from two utilities are used in modeling and forecasting. In addition, the effectiveness of a next 24 h ANN model in predicting 24 h load profile at one time was compared with the traditional next 1 h ANN model.>
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
Electric load forecasting: Literature survey and classification of methods
Alfares, H. K., & Nazeeruddin, M. (2002) · International Journal of Systems Science · doi:10.1080/00207720110067421 · Cited by 743
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
Deep Neural Network Based Demand Side Short Term Load Forecasting
Ryu, S., Noh, J., & Kim, H. (2016) · Energies · doi:10.3390/en10010003 · Cited by 409
Forecasting daily urban electric load profiles using artificial neural networks
Beccali, M., Cellura, M., Lo Brano, V., & Marvuglia, A. (2004) · Energy Conversion and Management · doi:10.1016/j.enconman.2004.01.006 · Cited by 253
Cicceri, G., Tricomi, G., D’Agati, L., Longo, F., Merlino, G., & Puliafito, A. (2023) · Sensors · doi:10.3390/s23094549 · Cited by 58
Metadata
- Authors
- Lu, C.-N., Wu, H.-T., & Vemuri, S.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1993
- Volume
- 8
- Issue
- 1
- Pages
- 336-342
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2148431083
- Cited by
- 270
- References
- 0
- FWCI
- 12.34
- 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: Neural network based short term load forecasting. https://app.cochat.ai/papers/10.1109/59.221223
- The paper
- Lu, C.-N., Wu, H.-T., & Vemuri, S. (1993). Neural network based short term load forecasting. IEEE Transactions on Power Systems. https://doi.org/10.1109/59.221223
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.
