Forecast of power generation for grid-connected photovoltaic system based on inclusion degree theory
Yingzi Li, Pingan Zhang, Shaoyi Wang · 2015
Large scale photovoltaic power system is one of the effective ways to use solar energy. Being the climate factors not stable, randomness, volatility and intermittent, the grid stability will be affected by the disturbance of PV system output. The forecast model of power generation for grid-connected PV system based on inclusion degree theory is divided into rule acquisition and generation forecast. In the rule acquisition module, the algorithms of inconsistent decision table and data reduction with unification attribute and attribute value have been used in the rule base of PV model temperature and power generation. In the power generation forecasting module, the algorithm of rule selection has been used in matching between forecast sample and rules. The cubic spline interpolation algorithm was restored a discrete forecast value to continuous. The results shows that this forecast model has a higher similarity to the actual PV systems and it also has a certain practicability and accuracy.