Paper Info
Title
Predictive Causality of Granger Test for Long Run Equilibrium to Photovoltaic System
Abstract
The high irradiance in tropical area is certainly favorable to photovoltaic (PV) power output, since the efficiency depends on the solar radiation intensity. However the increase of cells temperature causes conversely a fall of the yield of the modules. This decrease in the performance of PV modules due to temperature effect also causes proportional voltage decrease. This drop in performance is at the expense of destabilisation of the electrical network and if the electrical power output of modules is planned for a total grid injection. A time series may be useful in forecasting another time series and this statistical hypothesis test is called the Granger causality test. In this paper, we show that the Granger causality can be applied to PV parameters time series and an error correction model is used to determine a long term relationship of power output for PV systems.
Year
DOI
Venue
2019
10.1109/IDAACS.2019.8924303
2019 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS)
Keywords
Field
DocType
Autoregression,cointegration,stationary series,Granger,residuals,photovoltaic,grid injection
Autoregressive model,Electric power,Electrical network,Cointegration,Error correction model,Computer science,Control theory,Granger causality,Artificial intelligence,Photovoltaic system,Machine learning,Statistical hypothesis testing
Conference
Volume
ISBN
Citations 
2
978-1-7281-4070-4
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yannick Fanchette100.34
Ramenah, H.200.68
Philippe Casin300.34
Michel Benne400.34
Camel Tanougast512225.44
Kondo H. Adjallah600.34