Coherent Probabilistic Solar Power Forecasting

Keywords

Coherency; Hierarchical probabilistic forecasting; Quantile regression; Solar power forecasting; Time series modeling

Abstract

Solar power has been growing rapidly in recent years. Many countries have invested in solar energy technology, especially in Photovoltaic (PV) power generation. With the increased penetration level, solar power forecasting becomes more challenging. To cope with solar power uncertainties, probabilistic forecasting provides more information than traditional point forecasting. Moreover, multiple PV sites with spatial-temporal correlations need to be taken into account. To produce probabilistic forecasts, this paper applies quantile regression on top of time series models. Considering the coherency among multiple PV sites, a reconciliation is applied using a copula-based bottom-up method or proportion-based top-down method. Numerical results show that the proposed methods efficiently produce accurate and coherent probabilistic solar power forecasts.

Publication Date

8-17-2018

Publication Title

2018 International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2018 - Proceedings

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/PMAPS.2018.8440483

Socpus ID

85053157923 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85053157923

This document is currently not available here.

Share

COinS