The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Forecasting Hourly Electricity Demand Using Time-Varying Splines
Abstract
A method for modeling a changing periodic pattern is developed. The use of time-varying splines enables this to be done relatively parsimoniously. The method is applied in a model used to forecast hourly electricity demand, with the periodic movements being intradaily or intraweekly. The full model contains other components, including a temperature response, which is also modeled using splines.
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Citations
Cites
Forecasting, Structural Time Series Models and the Kalman Filter
Harvey, A. C. (1990) · doi:10.1017/cbo9781107049994 · Cited by 4,195
Self-citation
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
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
Forecasting loads and prices in competitive power markets
Bunn, D. W. (2000) · Proceedings of the IEEE · doi:10.1109/5.823996 · Cited by 458
Short-run forecasts of electricity loads and peaks
Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997) · International Journal of Forecasting · doi:10.1016/s0169-2070(97)00015-0 · Cited by 342
Metadata
- Authors
- Harvey, A., & Koopman, S. J.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1993-12
- Volume
- 88
- Issue
- 424
- Pages
- 1228-1236
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1975354406
- Cited by
- 237
- References
- 1
- FWCI
- 1.97
- Type
- journal article
- Language
- en
- Open access
- closed
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- Harvey, A., & Koopman, S. J. (1993). Forecasting Hourly Electricity Demand Using Time-Varying Splines. Journal of the American Statistical Association. https://doi.org/10.1080/01621459.1993.10476402
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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.
