Name
Abstract | ||
|---|---|---|
The multi-step prediction model based on partial least squares (PLS) is established to predict short-term load series with high embedding dimension in this paper, which refrains from cumulative error with local single-step linear model, and can cope with the multi-collinearity in the reconstructed phase space. In the model, PLS is used to model the dynamic evolution between the phase points and the corresponding future points. With research on the PLS theory, the model algorithm is put forward. Finally, the actual load series are used to test this model, and the results show that the model plays well in chaotic time series prediction, even if the embedding dimension is selected a big value. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1093/ietfec/e89-a.10.2740 | IEICE Transactions |
Keywords | Field | DocType |
high embedding dimension,phase point,short-term load forecasting,local single-step linear model,embedding dimension,pls theory,model algorithm,local partial,chaotic time series prediction,short-term load series,actual load series,multi-step prediction model,squares multi-step model | Partial least squares regression,Phase space,Load forecasting,Theoretical computer science,Artificial intelligence,Non-linear least squares,Chaos theory,Chaotic time series prediction,Embedding,Linear model,Algorithm,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
E89-A | 10 | 0916-8508 |
Citations | PageRank | References |
1 | 0.44 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zunxiong Liu | 1 | 2 | 0.84 |
| Xin Xie | 2 | 9 | 1.36 |
| Deyun Zhang | 3 | 52 | 14.54 |
| Haiyuan Liu | 4 | 1 | 2.13 |