Next Article in Journal
Building Applications, Opportunities and Challenges of Active Shading Systems: A State-of-the-Art Review
Previous Article in Journal
A Novel Topology of Hybrid HVDC Circuit Breaker for VSC-HVDC Application
Article Menu
Issue 10 (October) cover image

Export Article

Open AccessArticle
Energies 2017, 10(10), 1669; https://doi.org/10.3390/en10101669

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

1
School of Electric Engineering, Southwest Jiangtong University, Chengdu 610031, China
2
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610031, China
*
Author to whom correspondence should be addressed.
Academic Editor: Andrés G. Muñoz
Received: 30 August 2017 / Revised: 12 October 2017 / Accepted: 16 October 2017 / Published: 23 October 2017
(This article belongs to the Section Energy Storage and Application)
View Full-Text   |   Download PDF [2496 KB, uploaded 25 October 2017]   |  

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. View Full-Text
Keywords: PV power generation forecasting; extreme learning machine (ELM); bootstrap; prediction intervals (PIs); DC micro-grid system PV power generation forecasting; extreme learning machine (ELM); bootstrap; prediction intervals (PIs); DC micro-grid system
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Ni, Q.; Zhuang, S.; Sheng, H.; Wang, S.; Xiao, J. An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting. Energies 2017, 10, 1669.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top