BROWSE
Detail
Journal Article
Model‐agnostic online forecasting for PV power output
- Authors
- HyunYong Lee, Jun-Gi Lee, Nac-Woo Kim, Byung-Tak Lee
- Issue Date
- 2021-11
- Citation
- IET Renewable Power Generation, v.15, no.15, pp.3539-3551
- ISSN
- 1752-1416
- Publisher
- IET
- Language
- English
- Type
- Journal Article
- Abstract
- A reliable forecasting model is required for photovoltaic (PV) power output because solar energy is highly volatile. Another driver for the need of a reliable forecasting model is concept drift, which means that the statistical properties of the data change over time. In this paper, an online forecasting method to handle concept drift is proposed. First, the problem of forecasting in batch learning is transformed into a forecasting in online learning setting. Then, an online learning algorithm is applied, which is good for handling concept drift. Through experiments using the real-world data, it is shown that the method noticeably improves performance compared to the case where a trained model is used. Under various concept drift scenarios, the method improves performance by up to 87.3%. It is also shown that the re-training method (a representative existing method) has several limitations. This method requires several issues to be solved, such as selection of a proper window size, and this is evident through results showing different performance under different settings. In contrast, the method shows a reliable and desirable performance under various concept drift scenarios and thus outperforms the re-training method. The method improves performance by up to 79%.
- KSP Keywords
- Batch learning, Concept drift, Forecasting model, Online forecasting, Online learning algorithm, Over time, Photovoltaic (PV) Power, Re-training, Real-world data, Statistical properties, Window Size
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
