Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power Resource.

Abstract

The Irish government has, in recent years, been considering the possibility of using wind energy to meet a significant portion of Ireland's energy needs. This paper describes a project aimed at developing methods for the evaluation of Ireland's wind power resource. The authors consider estimation of the long-term average power output from a wind turbine generator at a site for which little data on wind speeds is available. Long-term records of wind speeds at the twelve synoptic meteorological stations are also used. Inference is based on a simple and parsimonious approximating model which accounts for the main features of wind speeds in Ireland, namely seasonal effects, spatial correlation, short-memory temporal autocorrelation, and long-memory temporal dependence. It syntheses deseasonalisation, kriging, ARMA modelling, and fractional differencing in a natural way. A simple kriging estimator performs well as a point estimator, and good interval estimators result from the model. The resulting procedure is easy to apply in practice.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1987
Accession Number
ADA191344

Entities

People

  • Adrian Raftery
  • John Haslett

Organizations

  • University of Washington

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Autocorrelation
  • Data Science
  • Energy
  • Estimators
  • Generators
  • Information Science
  • Intervals
  • Kinetic Energy
  • Maximum Likelihood Estimation
  • Operations Research
  • Square Roots
  • Statistical Algorithms
  • Statistics
  • Turbines
  • Weather Stations
  • Wind Energy
  • Wind Turbines

Readers

  • Aerosol Science/Aerosol Physics
  • Fluid Dynamics.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Space