LAUSR

The complex signal represented by power load is affected by many factors, so the signal components are very complicated. So that, it is difficult to obtain satisfactory prediction accuracy by using a single model for the complex signal. In this case, wavelet decomposition is used to decompose the power load into a series of sub signals. The low frequency sub signal is remarkably periodic, and the high frequency sub signals can prove to be chaotic signals. Then the signals of different characteristics are predicted by different models. For the low frequency sub signal, the support vector machine (SVM) is adopted. In SVM model, air temperature and week attributes are included in model inputs. Especially the week attribute is represented by a 3-bit binary encoding, which represents Monday to Sunday. For the chaotic high frequency sub signals, the chaotic local prediction (CLP) model is adopted. In CLP model, the embedding dimension and time delay are key parameters, which determines the prediction accuracy. In order to find the optimal parameters, a segmentation validation algorithm is proposed in this paper. The algorithm segments the known power load according to the time sequence. Then, based on the segmentation data, the optimal parameters are chosen based on the prediction accuracy. Compared with a single model, the prediction accuracy of the proposed algorithm is improved obviously, which proves the effectiveness.