Matches in SemOpenAlex for { <https://semopenalex.org/work/W4205132290> ?p ?o ?g. }
Showing items 1 to 72 of
72
with 100 items per page.
- W4205132290 abstract "Photovoltaic power generation is an important field of renewable energy development. However, when photovoltaic power generation is connected to the grid, the random fluctuation and intermittence of solar photovoltaic power generation caused by various environmental factors should be fully considered. Reliable prediction of photovoltaic power is a solution. Based on the analysis of two data analysis theories, which are cluster analysis and principal component analysis, this paper proposes a piecewise clustering method to improve the accuracy of RBF photovoltaic power prediction model. Firstly, the historical operational data after cleaning process are systematically clustered, and the corresponding target similar sample sets are obtained by two clustering methods. The RBF neural network algorithm is used to verify the prediction of the output power, which can effectively improve the stability and accuracy of the prediction results and simplify the number of input samples. Secondly, principal component analysis is carried out based on historical data, and the extracted principal component is taken as the input of RBF neural network photovoltaic power prediction model. It is proved by a case study that the proposed method can effectively improve the accuracy of prediction results and reduce the dimension of input samples, thus simplifying the network structure and shortening the learning time of neural networks. The effectiveness and reliability of the piecewise clustering method are verified by the case study." @default.
- W4205132290 created "2022-01-25" @default.
- W4205132290 creator A5004063144 @default.
- W4205132290 creator A5007067864 @default.
- W4205132290 creator A5013319149 @default.
- W4205132290 creator A5036441221 @default.
- W4205132290 creator A5044323586 @default.
- W4205132290 creator A5066126788 @default.
- W4205132290 creator A5085028455 @default.
- W4205132290 creator A5085959885 @default.
- W4205132290 date "2021-07-18" @default.
- W4205132290 modified "2023-09-23" @default.
- W4205132290 title "Piecewise Clustering Prediction Model of Distributed Photovoltaic Power Based on Principal Component Analysis" @default.
- W4205132290 doi "https://doi.org/10.1109/icpsasia52756.2021.9621639" @default.
- W4205132290 hasPublicationYear "2021" @default.
- W4205132290 type Work @default.
- W4205132290 citedByCount "0" @default.
- W4205132290 crossrefType "proceedings-article" @default.
- W4205132290 hasAuthorship W4205132290A5004063144 @default.
- W4205132290 hasAuthorship W4205132290A5007067864 @default.
- W4205132290 hasAuthorship W4205132290A5013319149 @default.
- W4205132290 hasAuthorship W4205132290A5036441221 @default.
- W4205132290 hasAuthorship W4205132290A5044323586 @default.
- W4205132290 hasAuthorship W4205132290A5066126788 @default.
- W4205132290 hasAuthorship W4205132290A5085028455 @default.
- W4205132290 hasAuthorship W4205132290A5085959885 @default.
- W4205132290 hasConcept C119599485 @default.
- W4205132290 hasConcept C121332964 @default.
- W4205132290 hasConcept C127413603 @default.
- W4205132290 hasConcept C134306372 @default.
- W4205132290 hasConcept C154945302 @default.
- W4205132290 hasConcept C163258240 @default.
- W4205132290 hasConcept C164660894 @default.
- W4205132290 hasConcept C168167062 @default.
- W4205132290 hasConcept C27438332 @default.
- W4205132290 hasConcept C33923547 @default.
- W4205132290 hasConcept C41008148 @default.
- W4205132290 hasConcept C41291067 @default.
- W4205132290 hasConcept C62520636 @default.
- W4205132290 hasConcept C73555534 @default.
- W4205132290 hasConcept C97355855 @default.
- W4205132290 hasConceptScore W4205132290C119599485 @default.
- W4205132290 hasConceptScore W4205132290C121332964 @default.
- W4205132290 hasConceptScore W4205132290C127413603 @default.
- W4205132290 hasConceptScore W4205132290C134306372 @default.
- W4205132290 hasConceptScore W4205132290C154945302 @default.
- W4205132290 hasConceptScore W4205132290C163258240 @default.
- W4205132290 hasConceptScore W4205132290C164660894 @default.
- W4205132290 hasConceptScore W4205132290C168167062 @default.
- W4205132290 hasConceptScore W4205132290C27438332 @default.
- W4205132290 hasConceptScore W4205132290C33923547 @default.
- W4205132290 hasConceptScore W4205132290C41008148 @default.
- W4205132290 hasConceptScore W4205132290C41291067 @default.
- W4205132290 hasConceptScore W4205132290C62520636 @default.
- W4205132290 hasConceptScore W4205132290C73555534 @default.
- W4205132290 hasConceptScore W4205132290C97355855 @default.
- W4205132290 hasLocation W42051322901 @default.
- W4205132290 hasOpenAccess W4205132290 @default.
- W4205132290 hasPrimaryLocation W42051322901 @default.
- W4205132290 hasRelatedWork W1803249363 @default.
- W4205132290 hasRelatedWork W2006750137 @default.
- W4205132290 hasRelatedWork W2018199316 @default.
- W4205132290 hasRelatedWork W2108518071 @default.
- W4205132290 hasRelatedWork W2125310307 @default.
- W4205132290 hasRelatedWork W2358364538 @default.
- W4205132290 hasRelatedWork W2362802221 @default.
- W4205132290 hasRelatedWork W2387004660 @default.
- W4205132290 hasRelatedWork W2392472005 @default.
- W4205132290 hasRelatedWork W3092777184 @default.
- W4205132290 isParatext "false" @default.
- W4205132290 isRetracted "false" @default.
- W4205132290 workType "article" @default.