Feature selection for accurate short-term forecasting of local wind-speed

Usman Sanusi, David Corne

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

There is increasing demand for accurate short-term forecasting of weather conditions at specified locations. This demand arises partly from the growing numbers of renewable energy facilities. In order successfully to integrate renewable energy supplies with grid sources, the short term (e.g. next 24 hrs) output profile of the renewable system needs to be forecast as accurately as possible, to avoid over-reliance on fossil fuels at times when renewables are available, and to avoid deficit in supply when they aren't. In particular, the inherent variability in wind-speed poses an additional challenge. Several approaches for wind-speed forecasting have previously been developed, ranging from simple time series analysis to the use of a combination of global weather forecasting, computational fluid dynamics and machine learning methods. For localized forecasting, statistical methods that rely on historical location data come to the forefront. Recent such work (building localized forecast models with multivariate linear regression) has found that accuracy can gain significantly by learning from multiple types of local weather features. Here, we build on that work by investigating the potential benefits of simple additional 'derived' features, such as the gradient in wind-speed or other variables. Following extensive experimentation using data from sites in Nigeria (primarily), Scotland and Italy, we conclude that the ideal forecasting model for a given location will use a judicious combination of direct and derived features.

Original languageEnglish
Title of host publication2015 IEEE 8th International Workshop on Computational Intelligence and Applications (IWCIA)
PublisherIEEE
Pages121-126
Number of pages6
ISBN (Print)9781479998869
DOIs
Publication statusPublished - 2015
Event8th IEEE International Workshop on Computational Intelligence and Applications 2015 - Hiroshima, Japan
Duration: 6 Nov 20157 Nov 2015

Conference

Conference8th IEEE International Workshop on Computational Intelligence and Applications 2015
Abbreviated titleIWCIA 2015
CountryJapan
CityHiroshima
Period6/11/157/11/15

Keywords

  • feature derivation
  • feature selection
  • forecasting
  • prediction
  • regression
  • renewables

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization
  • Artificial Intelligence
  • Computer Science Applications

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