Incremental Adaptive Time Series Prediction for Power Demand Forecasting
| Autor: | Viera Rozinajová, Petra Vrablecová, Anna Bou Ezzeddine |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Concept drift
Computer science business.industry Real-time computing Distributor Process (computing) Contrast (statistics) 02 engineering and technology Grid Power (physics) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Electricity Time series business |
| Zdroj: | Data Mining and Big Data ISBN: 9783319618449 DMBD |
| DOI: | 10.1007/978-3-319-61845-6_9 |
| Popis: | Accurate power demand forecasts can help power distributors to lower differences between contracted and demanded electricity and minimize the imbalance in grid and related costs. Our forecasting method is designed to process continuous stream of data from smart meters incrementally and to adapt the prediction model to concept drifts in power demand. It identifies drifts using a condition based on an acceptable distributor’s daily imbalance. Using only the most recent data to adapt the model (in contrast to all historical data) and adapting the model only when the need for it is detected (in contrast to creating a whole new model every day) enables the method to handle stream data. The proposed model shows promising results. |
| Databáze: | OpenAIRE |
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