The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Short-term load forecasting via ARMA model identification including non-gaussian process considerations
Abstract
In this paper, the short-term load forecast by use of autoregressive moving average (ARMA) model including non-Gaussian process considerations is proposed. In the proposed method, the concept of cumulant and bispectrum are embedded into the ARMA model in order to facilitate Gaussian and non-Gaussian process. With embodiment of a Gaussianity verification procedure, the forecasted model is identified more appropriately. Therefore, the performance of ARMA model is better ensured, improving the load forecast accuracy significantly. The proposed method has been applied on a practical system and the results are compared with other published techniques.
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Cites
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Short-term hourly load forecasting using time-series modeling with peak load estimation capability
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Identification of nonminimum phase systems using higher order statistics
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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
Sadaei, H. J., de Lima e Silva, P. C., Guimarães, F. G., & Lee, M. H. (2019) · Energy · doi:10.1016/j.energy.2019.03.081 · Cited by 325
Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting
Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., & Torres, J. (2018) · Energies · doi:10.3390/en11040949 · Cited by 299
Deep learning for time series forecasting: The electric load case
Gasparin, A., Lukovic, S., & Alippi, C. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12060 · Cited by 293
Metadata
- Authors
- Huang, S.-J., & Shih, K.-R.
- Venue
- IEEE Transactions on Power Systems
- Published
- 2003-05
- Volume
- 18
- Issue
- 2
- Pages
- 673-679
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2133720763
- Cited by
- 675
- References
- 16
- FWCI
- 13.41
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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