The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting
Abstract
In this paper, a new particle swarm optimization (PSO) approach to identifying the autoregressive moving average with exogenous variable (ARMAX) model for one-day to one-week ahead hourly load forecasts was proposed. Owing to the inherent nonlinear characteristics of power system loads, the surface of the forecasting error function possesses many local minimum points. Solutions of the gradient search-based stochastic time series (STS) technique may, therefore, stall at the local minimum points, which lead to an inadequate model. By simulating a simplified social system, the PSO algorithm offers the capability of converging toward the global minimum point of a complex error surface. The proposed PSO has been tested on the different types of Taiwan Power (Taipower) load data and compared with the evolutionary programming (EP) algorithm and the traditional STS method. Testing results indicate that the proposed PSO has high-quality solution, superior convergence characteristics, and shorter computation time.
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 look at the statistical model identification
Akaike, H. (1974) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1974.1100705 · Cited by 51,879
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
Cited by
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
Electric load forecasting methods: Tools for decision making
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009) · European Journal of Operational Research · doi:10.1016/j.ejor.2009.01.062 · Cited by 549
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
Applications of Metaheuristics in Reservoir Computing Techniques: A Review
Bala, A., Ismail, I., Ibrahim, R., & Sait, S. M. (2018) · IEEE Access · doi:10.1109/access.2018.2873770 · Cited by 53
Metadata
- Authors
- Huang, C.-M., Huang, C.-J., & Wang, M.-L.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2005-05
- Volume
- 20
- Issue
- 2
- Pages
- 1126-1133
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2136325969
- Cited by
- 260
- References
- 2
- FWCI
- 13.67
- 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 Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting. https://app.cochat.ai/papers/10.1109/tpwrs.2005.846106
- The paper
- Huang, C.-M., Huang, C.-J., & Wang, M.-L. (2005). A Particle Swarm Optimization to Identifying the ARMAX Model for Short-Term Load Forecasting. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.2005.846106
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.
