Using conditional kernel density estimation for wind power density forecasting

Jooyong Jeon, James W. Taylor

Research output: Contribution to journalArticlepeer-review

131 Citations (Scopus)
200 Downloads (Pure)

Abstract

Of the various renewable energy resources, wind power is widely recognized as one of the most promising. The management of wind farms and electricity systems can benefit greatly from the availability of estimates of the probability distribution of wind power generation. However, most research has focused on point forecasting of wind power. In this article, we develop an approach to producing density forecasts for the wind power generated at individual wind farms. Our interest is in intraday data and prediction from 1 to 72 hours ahead. We model wind power in terms of wind speed and wind direction. In this framework, there are two key uncertainties. First, there is the inherent uncertainty in wind speed and direction, and we model this using a bivariate vector autoregressive moving average-generalized autoregressive conditional heteroscedastic (VARMA-GARCH) model, with a Student t error distribution, in the Cartesian space of wind speed and direction. Second, there is the stochastic nature of the relationship of wind power to wind speed (described by the power curve), and to wind direction. We model this using conditional kernel density (CKD) estimation, which enables a nonparametric modeling of the conditional density of wind power. Using Monte Carlo simulation of the VARMA-GARCH model and CKD estimation, density forecasts of wind speed and direction are converted to wind power density forecasts. Our work is novel in several respects: previous wind power studies have not modeled a stochastic power curve; to accommodate time evolution in the power curve, we incorporate a time decay factor within the CKD method; and the CKD method is conditional on a density, rather than a single value. The new approach is evaluated using datasets from four Greek wind farms.
Original languageEnglish
Pages (from-to)66-79
Number of pages14
JournalJournal of the American Statistical Association
Volume107
Issue number497
Early online date11 Jun 2012
DOIs
Publication statusPublished - 2012

Keywords

  • Sustainability
  • Bivariate GARCH
  • Kernel estimation
  • Predictive distribution
  • Wind direction
  • Wind energy
  • Wind speed

Fingerprint Dive into the research topics of 'Using conditional kernel density estimation for wind power density forecasting'. Together they form a unique fingerprint.

Cite this