Short-term local prediction of wind speed and wind power based on singular spectrum analysis and locality-sensitive hashing

Abstract

With the growing penetration of wind power in power systems, more accurate prediction of wind speed and wind power is required for real-time scheduling and operation. In this paper, a novel forecast model for shortterm prediction of wind speed and wind power is proposed, which is based on singular spectrum analysis (SSA) and locality-sensitive hashing (LSH). To deal with the impact of high volatility of the original time series, SSA is applied to decompose it into two components: the mean trend, which represents the mean tendency of the original time series, and the fluctuation component, which reveals the stochastic characteristics. Both components are reconstructed in a phase space to obtain mean trend segments and fluctuation component segments. After that, LSH is utilized to select similar segments of the mean trend segments, which are then employed in local forecasting, so that the accuracy and efficiency of prediction can be enhanced. Finally, support vector regression is adopted for prediction, where the training input is the synthesis of the similar mean trend segments and the corresponding fluctuation component segments. Simulation studies are conducted on wind speed and wind power time series from four databases, and the final results demonstrate that the proposed model is more accurate and stable in comparison with other models.

Multi-objective interval prediction of wind power based on conditional copula function

“…In Fig. 4d, only four points (2,8,64,67) in the predicted segment do not meet the prediction requirements. It can be observed that the method of this paper is still the most reliable.…”

“…In Fig. 4b, only four points (1,2,64,67) in the predicted segment do not meet the prediction requirements. In Fig.…”

“…Therefore, F j tþ1 ; p j  à j¼1; 2; ...; J is the discrete probability distribution function of F tþ1 x F tþ1 À Á based on (2), which is the discrete conditional copula function.…”

“…China is rich in wind resources [1,2] and has adopted a national policy to vigorously develop wind power. However, wind power has strong randomness and volatility and is difficult to accurately predict [3][4][5][6][7][8], thus making wind power prediction a current hotspot in research.…”

Progression of chronic kidney disease in African Americans with type 2 diabetes mellitus using topology learning in electronic medical records

“…That is, for any distinct trajectory pattern, when there are more than 150 patients in the cohort sharing this pattern, the prediction for new patients who also have this trajectory pattern will be accurate. Since patient numbers in EMR data can easily reach into the million range, it is promising to build a large database of trajectory patterns in existing patients and use locality sensitive hashing (28)(29)(30)(31) and approximate nearest neighbor query techniques (32,33) to predict disease progressions in new patients. clusters color-coded by kidney functions (blue: good, red: impaired); clusters enriched with encounters of patients of high-or low-risk APOL1 genotypes (middle and right, correspondingly).…”