The high penetration of solar PV generations brings about significant challenges for decision-makers of power system operation due to high volatility and uncertainty it involves. In recent years, it has been demonstrated by many researchers that the probabilistic interval forecast could significantly facilitate some decision-making cases, such as storage optimization, market bidding, reserves setting, as it can provide the uncertainty information associated with the point estimations. This paper proposes a nonparametric conditional interval forecast method for PV power generation which can capture the interdependence among the real power output and their point forecasts within all forecasting horizons of interests. The proposed model is tested using the dataset of PV generation power measurements and day-ahead point forecasts in Belgium. The results based on reliability and interval score performance metrics illustrate the effectiveness of the proposed model.
|Title of host publication||IEEE Power and Energy Society General Meeting|
|Publisher||IEEE, Institute of Electrical and Electronics Engineers|
|Publication status||Published - 10 Nov 2016|
|Event||2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States|
Duration: 17 Jul 2016 → 21 Jul 2016
|Conference||2016 IEEE Power and Energy Society General Meeting, PESGM 2016|
|Period||17/07/16 → 21/07/16|