A Set-Membership approach to short-term electric load forecasting
This work presents a model for the short-term forecast of electric load, based on Set-Membership techniques. The model is formed by a periodic component and an adaptive non-linear autoregressive component. The identifications set of the non-linear model is increased at each estimation step. The mod...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Spanish |
| Published: |
Universitat Politècnica de València
2019-09-01
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| Series: | Revista Iberoamericana de Automática e Informática Industrial RIAI |
| Subjects: | |
| Online Access: | https://polipapers.upv.es/index.php/RIAI/article/view/9819 |
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| Summary: | This work presents a model for the short-term forecast of electric load, based on Set-Membership techniques. The model is formed by a periodic component and an adaptive non-linear autoregressive component. The identifications set of the non-linear model is increased at each estimation step. The model is evaluated in a case study with more than 13.000 samples of hourly sampled energy demand, registered during three years at a rural town in Colombia. The performance of the estimator is evaluated and confronted to a linear autoregressive model and a standard Set-Membership model with fixed identification set. Results show that the proposed estimator is able to predict demand with an RMS error below 2.5% for validation data, using just a 5% of the available dataset for the model identification. |
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| ISSN: | 1697-7912 1697-7920 |