- Title
- A new solar power prediction method based on feature clustering and hybrid-classification-regression forecasting
- Creator
- Nejati, Maryam; Amjady, Nima
- Date
- 2022
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/191342
- Identifier
- vital:17773
- Identifier
-
https://doi.org/10.1109/TSTE.2021.3138592
- Identifier
- ISSN:1949-3029
- Abstract
- Solar generation systems are globally extending in terms of scale and number, which highlights the increasing importance of solar power forecast. In this paper, a day-ahead solar power prediction method is proposed including 1) a novel feature selecting/clustering approach based on relevancy and redundancy criteria and 2) an innovative hybrid-classification-regression forecasting engine. The proposed feature selecting/clustering approach filters out irrelevant features and partitions relevant features to two separate subsets to decrease the redundancy of features. Each of these two subsets is separately trained by one forecasting engine and the final solar power prediction of the proposed method is obtained by a relevancy-based combination of these two forecasts. The proposed forecasting engine classifies the historical data based on the learnability of its constituent regression models and assigns each class of training samples to one regression model. Each regression model predicts the outputs of the test samples that belong to its class. The effectiveness of the proposed solar power prediction method is illustrated by testing on two real-world solar farms.
- Publisher
- IEEE
- Relation
- IEEE transactions on sustainable energy Vol. 13, no. 2 (2022), p. 1188-1198
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright IEEE
- Subject
- Atmospheric modeling; Classification; Clustering; Data models; Engines; Farms; Feature extraction; Feature selecting/ clustering; Forecasting; Historical account; Hybrid-classification-regression forecasting engine; Learnability; Photovoltaic cells; Predictions; Predictive models; Redundancy; Regression analysis; Regression models; Solar energy; Solar power; Solar power generation; Solar power prediction; Weather forecasting; 4009 Electrical engineering; 4009 Electronics, sensors and digital hardware
- Reviewed
- Hits: 1905
- Visitors: 1901
- Downloads: 0