Next Article in Journal
An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range
Previous Article in Journal
Norway as a Battery for the Future European Power System—Impacts on the Hydropower System
Article Menu
Issue 12 (December) cover image

Export Article

Open AccessArticle
Energies 2017, 10(12), 1988; doi:10.3390/en10121988

Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy

1
Microgrid Platform R&D Center, Goldwind Science and Etechwin Electric Co., Ltd. BDA, Beijing 100176, China
2
State Grid Hebei Electric Power Company, Shijiazhuang 050022, China
3
School of Electrical and Electronic Engineering, North China Electric Power University, Changping District, Beijing 102206, China
*
Author to whom correspondence should be addressed.
Received: 31 October 2017 / Revised: 16 November 2017 / Accepted: 17 November 2017 / Published: 4 December 2017
(This article belongs to the Section Electrical Power and Energy System)
View Full-Text   |   Download PDF [3508 KB, uploaded 4 December 2017]   |  

Abstract

The power generated by wind generators is usually associated with uncertainties, due to the intermittency of wind speed and other weather variables. This creates a big challenge for transmission system operators (TSOs) and distribution system operators (DSOs) in terms of connecting, controlling and managing power networks with high-penetration wind energy. Hence, in these power networks, accurate wind power forecasts are essential for their reliable and efficient operation. They support TSOs and DSOs in enhancing the control and management of the power network. In this paper, a novel two-stage hybrid approach based on the combination of the Hilbert-Huang transform (HHT), genetic algorithm (GA) and artificial neural network (ANN) is proposed for day-ahead wind power forecasting. The approach is composed of two stages. The first stage utilizes numerical weather prediction (NWP) meteorological information to predict wind speed at the exact site of the wind farm. The second stage maps actual wind speed vs. power characteristics recorded by SCADA. Then, the wind speed forecast in the first stage for the future day is fed to the second stage to predict the future day’s wind power. Comparative selection of input-data parameter sets for the forecasting model and impact analysis of input-data dependency on forecasting accuracy have also been studied. The proposed approach achieves significant forecasting accuracy improvement compared with three other artificial intelligence-based forecasting approaches and a benchmark model using the smart persistence method. View Full-Text
Keywords: artificial neural network; forecasting; genetic algorithm; Hilbert-Huang transform; NWP (numerical weather prediction); SCADA (supervisory control and data acquisition); wind power artificial neural network; forecasting; genetic algorithm; Hilbert-Huang transform; NWP (numerical weather prediction); SCADA (supervisory control and data acquisition); wind power
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zheng, D.; Shi, M.; Wang, Y.; Eseye, A.T.; Zhang, J. Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy. Energies 2017, 10, 1988.

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