Next Article in Journal
A New Vertical JFET Power Device for Harsh Radiation Environments
Next Article in Special Issue
Risk-Based Dynamic Security Assessment for Power System Operation and Operational Planning
Previous Article in Journal
A Parameter Selection Method for Wind Turbine Health Management through SCADA Data
Previous Article in Special Issue
Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration
Article Menu
Issue 2 (February) cover image

Export Article

Open AccessArticle
Energies 2017, 10(2), 252; doi:10.3390/en10020252

Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data

Department of Engineering, University of Sannio, 82100 Benevento, Italy
*
Authors to whom correspondence should be addressed.
Academic Editor: Gianfranco Chicco
Received: 12 January 2017 / Accepted: 13 February 2017 / Published: 20 February 2017
(This article belongs to the Special Issue Advances in Power System Operations and Planning)
View Full-Text   |   Download PDF [5480 KB, uploaded 22 February 2017]   |  

Abstract

The massive penetration of wind generators in electrical power systems asks for effective wind power forecasting tools, which should be high reliable, in order to mitigate the effects of the uncertain generation profiles, and fast enough to enhance power system operation. To address these two conflicting objectives, this paper advocates the role of knowledge discovery from big-data, by proposing the integration of adaptive Case Based Reasoning models, and cardinality reduction techniques based on Partial Least Squares Regression, and Principal Component Analysis. The main idea is to learn from a large database of historical climatic observations, how to solve the windforecasting problem, avoiding complex and time-consuming computations. To assess the benefits derived by the application of the proposed methodology in complex application scenarios, the experimental results obtained in a real case study will be presented and discussed. View Full-Text
Keywords: wind power forecasting; knowledge discovery; big data; case-based reasoning; machine learning wind power forecasting; knowledge discovery; big data; case-based reasoning; machine learning
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

De Caro, F.; Vaccaro, A.; Villacci, D. Spatial and Temporal Wind Power Forecasting by Case-Based Reasoning Using Big-Data. Energies 2017, 10, 252.

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