The DOI matches its Crossref record.
Comparative Models for Electrical Load Forecasting
Abstract
This reference describes the methods used to forecast loads on public utility systems, featuring modern documentation and forecasting techniques for lead times of up to approximately 24 hours. Methods include automatic adaptive, univariate, and multivariate. The volume is divided into three parts: an introduction to the general economic and operational decision-making contexts and methods of short-term forecasting; six contributed insights into a wide range of model developments in load forecasting; and further insights into adjacent fields, including essays on the econometric perspective to load forecasting and the very short-term implications of state-estimation for data validation.
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
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
Weron, R. (2006) · doi:10.1002/9781118673362 · Cited by 708
Analysis and evaluation of five short-term load forecasting techniques
Moghram, I., & Rahman, S. (1989) · IEEE Transactions on Power Systems · doi:10.1109/59.41700 · Cited by 706
Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A. J., Lloret, J., & Massana, J. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2014.032014.00094 · Cited by 506
Forecasting loads and prices in competitive power markets
Bunn, D. W. (2000) · Proceedings of the IEEE · doi:10.1109/5.823996 · Cited by 458
Self-citationA review of deep learning with special emphasis on architectures, applications and recent trends
Sengupta, S., Basak, S., Saikia, P., Paul, S., Tsalavoutis, V., Atiah, F., Ravi, V., & Peters, A. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105596 · Cited by 454
Metadata
- Authors
- Harris, T., Bunn, D. W., & Farmer, E. D.
- Venue
- Technometrics
- Publisher
- JSTOR
- Published
- 1986-11
- Volume
- 28
- Issue
- 4
- Pages
- 403
- ISSN
- 0040-1706 (ISSN-L)
- OpenAlex
- W2034353796
- Cited by
- 265
- References
- 0
- FWCI
- 2.09
- 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: Comparative Models for Electrical Load Forecasting. https://app.cochat.ai/papers/10.2307/1268996
- The paper
- Harris, T., Bunn, D. W., & Farmer, E. D. (1986). Comparative Models for Electrical Load Forecasting. Technometrics. https://doi.org/10.2307/1268996
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.
