An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Qiang Ni, Shengxian Zhuang, Hanmin Sheng, Song Wang and Jian Xiao
Additional contact information
Qiang Ni: School of Electric Engineering, Southwest Jiangtong University, Chengdu 610031, China
Shengxian Zhuang: School of Electric Engineering, Southwest Jiangtong University, Chengdu 610031, China
Hanmin Sheng: School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610031, China
Song Wang: School of Electric Engineering, Southwest Jiangtong University, Chengdu 610031, China
Jian Xiao: School of Electric Engineering, Southwest Jiangtong University, Chengdu 610031, China

Energies, 2017, vol. 10, issue 10, 1-16

Abstract: High quality photovoltaic (PV) power prediction intervals (PIs) are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

Keywords: PV power generation forecasting; extreme learning machine (ELM); bootstrap; prediction intervals (PIs); DC micro-grid system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/10/1669/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/10/1669/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:10:p:1669-:d:116114

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().