Please use this identifier to cite or link to this item:
Title: Short-term PV generation forecasting using quantile regression averaging
Authors: Tripathy D.S.
Prusty B.R.
Jena D.
Issue Date: 2020
Citation: 2020 IEEE International Conference on Power Systems Technology, POWERCON 2020 , Vol. , , p. -
Abstract: The globally increasing demand for energy to carry out the various day-to-day activities needs renewable sources in conjunction with existing power plants. PV technology has seen tremendous growth over the past decades. However, the integration of PV generation to the power systems invites numerous planning and operational challenges. In the short-term, the real-time operation of PV-integrated power systems requires the characterization of the uncertainties associated with the PV generation. A probabilistic framework, such as the quantile regression averaging (QRA), has been successful in forecasting load power and electricity spot prices. This paper applies QRA to accomplish a probabilistic forecast of PV generation using its historical record from a rooftop installation at Lincoln, USA. This paper's main contribution is the use of two appropriate individual point forecasters, i.e., autoregressive conditional heteroscedastic and multiple linear regression models, to complement each other and make accurate quantile forecasts. The proposed model is used in the short-term forecasting of PV generation for the four major seasons up to two weeks ahead. A detailed result analysis shows that the combination of both models improves overall forecasting performance rather than using any of the models alone. © 2020 IEEE.
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.