Conference paper
Probabilistic forecasting of wind power at the minute time-scale with Markov-switching autoregressive models
Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed methodology is based on a Markov-switching autoregressive model with time-varying coefficients.
An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute.
The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2008 |
| Pages: | 1-8 |
| Proceedings: | 10th International Conference on Probabilistic Methods Applied to Power Systems |
| ISBN: | 193432521X , 193432521x , 9781934325216 , 1934325406 and 9781934325407 |
| Types: | Conference paper |
| ORCIDs: | Pinson, Pierre and Madsen, Henrik |
1-step ahead forecasting exercise AC generators Character generation Denmark Fluctuations Markov processes Markov-switching autoregressive models Power generation Predictive models Production Wind energy Wind energy generation Wind farms Wind forecasting autoregressive processes control strategy forecasting methodology large offshore wind farms load forecasting management strategy power system management probabilistic forecasting regime switching short-term fluctuations statistical modelling time 10 min time-series very short-term power fluctuations wind generation wind power wind power plants
