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The Time Series Approach to Short Term Load Forecasting
Abstract
The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function and the partial autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.
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Citations
Cited by
Bouktif, S., Fiaz, A., Ouni, A., & Serhani, M. (2018) · Energies · doi:10.3390/en11071636 · Cited by 915
Deep Neural Network Based Demand Side Short Term Load Forecasting
Ryu, S., Noh, J., & Kim, H. (2016) · Energies · doi:10.3390/en10010003 · Cited by 409
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
Al-Hamadi, H. M., & Soliman, S. A. (2004) · Electric Power Systems Research · doi:10.1016/s0378-7796(03)00150-0 · Cited by 282
Chen, J.-F., Wang, W.-M., & Huang, C.-M. (1995) · Electric Power Systems Research · doi:10.1016/0378-7796(95)00977-1 · Cited by 253
Metadata
- Authors
- Hagan, M. T., & Behr, S. M.
- Venue
- IEEE Transactions on Power Systems
- Published
- 1987
- Volume
- 2
- Issue
- 3
- Pages
- 785-791
- ISSN
- 0885-8950 (ISSN-L)
- OpenAlex
- W2121844625
- Cited by
- 546
- References
- 0
- FWCI
- 2.56
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: The Time Series Approach to Short Term Load Forecasting. https://app.cochat.ai/papers/10.1109/tpwrs.1987.4335210
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- Hagan, M. T., & Behr, S. M. (1987). The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems. https://doi.org/10.1109/tpwrs.1987.4335210
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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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