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A Hybrid Approach for Day-Ahead Forecast of PV Power Generation
- Source :
- IFAC-PapersOnLine. 51:634-638
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- In the past years, the applications of solar energy have grown significantly in electricity generation. However, the fluctuations of PV power output create different negative impacts on reliability, stability, and dispatch in the connecting grid. The exact PV power generation forecast is thus crucial to stabilize the operation of a power grid. This paper presents a radial basis function neural network with decoupling method for day-ahead PV power generation forecast. Results are compared with autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN), and radial basis function neural network (RBFNN), and the actual measured PV power outputs. It shows that the proposed model leads to more accurate and the computational efficient forecast on PV output.
- Subjects :
- business.industry
Computer science
020209 energy
02 engineering and technology
Decoupling (cosmology)
021001 nanoscience & nanotechnology
Solar energy
Grid
Hybrid approach
Electricity generation
Control and Systems Engineering
Control theory
0202 electrical engineering, electronic engineering, information engineering
Autoregressive integrated moving average
Power grid
0210 nano-technology
business
Decoupling (electronics)
Pv power
Subjects
Details
- ISSN :
- 24058963
- Volume :
- 51
- Database :
- OpenAIRE
- Journal :
- IFAC-PapersOnLine
- Accession number :
- edsair.doi...........05fa3ca5b0430761292bd6495a88b441