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Deep belief rule based photovoltaic power forecasting method with interpretability.
- Source :
-
Scientific reports [Sci Rep] 2022 Aug 24; Vol. 12 (1), pp. 14467. Date of Electronic Publication: 2022 Aug 24. - Publication Year :
- 2022
-
Abstract
- Accurate prediction of photovoltaic (PV) output power is of great significance for reasonable scheduling and development management of power grids. In PV power generation prediction system, there are two problems: the uncertainty of PV power generation and the inexplicability of the prediction result. The belief rule base (BRB) is a rule-based modeling method and can deal with uncertain information. Moreover, the modeling process of BRB has a certain degree of interpretability. However, rule explosion and the inexplicability of the optimized model limit the modeling ability of BRB in complex systems. Thus, a PV output power prediction model is proposed based on a deep belief rule base with interpretability (DBRB-I). In the DBRB-I model, the deep BRB structure is constructed to solve the rule explosion problem, and inefficient rules are simplified by a sensitivity analysis of the rules, which reduces the complexity of the model. Moreover, to ensure that the interpretability of the model is not destroyed, a new optimization method based on the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is designed. Finally, a case study of the prediction of PV output power is conducted to illustrate the effectiveness of the proposed method.<br /> (© 2022. The Author(s).)
Details
- Language :
- English
- ISSN :
- 2045-2322
- Volume :
- 12
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Scientific reports
- Publication Type :
- Academic Journal
- Accession number :
- 36002587
- Full Text :
- https://doi.org/10.1038/s41598-022-18820-6