The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Short-Term Load Forecasting Using General Exponential Smoothing
Abstract
An essential component of a comprehensive, real-time control center for power systems is a method for the calculation of short-term load forecasts. This paper explores the use of general exponential smoothing to develop an adaptive forecasting system based on observed values of integrated hourly demand. A model is developed which offers high accuracy and operational simplicity. Forecasts of hourly MWH load with lead times of one to twenty-four hours are computed at hourly intervals throughout the week. A pragmatic viewpoint is maintained throughout the paper, which includes an analysis of two years of hourly load data, test results for the method developed, and discussions of adjustments for holidays and weather disturbances.
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
Cites
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Cited by
Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing
De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011) · Journal of the American Statistical Association · doi:10.1198/jasa.2011.tm09771 · Cited by 1,049
Zheng, H., Yuan, J., & Chen, L. (2017) · Energies · doi:10.3390/en10081168 · Cited by 635
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Short-term load forecasting using lifting scheme and ARIMA models
Lee, C.-M., & Ko, C.-N. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.11.033 · Cited by 438
Metadata
- Authors
- Christiaanse, W.
- Venue
- IEEE Transactions on Power Apparatus and Systems
- Published
- 1971-03
- Volume
- PAS-90
- Issue
- 2
- Pages
- 900-911
- ISSN
- 0018-9510 (ISSN-L)
- OpenAlex
- W2076163358
- Cited by
- 500
- References
- 1
- FWCI
- 3.84
- 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: Short-Term Load Forecasting Using General Exponential Smoothing. https://app.cochat.ai/papers/10.1109/tpas.1971.293123
- The paper
- Christiaanse, W. (1971). Short-Term Load Forecasting Using General Exponential Smoothing. IEEE Transactions on Power Apparatus and Systems. https://doi.org/10.1109/tpas.1971.293123
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.