Use of turbine-level data for improved wind power forecasting

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

191 Downloads (Pure)

Abstract

Short-term wind power forecasting is based on modelling the complex relationship between the weather forecasts and wind farm power production. To date, efforts to improve wind power forecasts have focused on improving Numerical Weather Prediction and wind farm power curve models. However, utility-scale wind farms cover large areas meaning that a single power curve model may struggle to represent the collective behaviour of large numbers of wind turbines. Contemporary statistical techniques are capable of processing large volumes of data, enabling the assimilation of measurements from individual wind turbines to construct a more detailed representation of wind farm power generation. Here, three state-of-the-art techniques are applied to forecast wind farm power production 1) directly from numerical weather predictions, and 2) by aggregating forecasts for individual wind turbines. Furthermore, it is observed that some wind turbines are better predictors than others and an aggregation process based on conditional weighting is proposed.
In case studies of two large wind farms in the UK, it is shown that wind farm power forecasts comprising a conditional weighted sum of turbine-level predictions are superior to a direct wind farm forecast for horizons up to 48 hours ahead. Specifically, performance of the best-performing benchmark, the gradient boosting machine, is improved by 12% at Clyde South wind farm and by 6% at Gordonbush.
Original languageEnglish
Title of host publication12th IEEE PES PowerTech Conference
Subtitle of host publicationTowards and Beyond Sustainable Energy Systems
Place of PublicationPiscataway, NJ
PublisherIEEE
Publication statusAccepted/In press - 7 Apr 2017
Event12th IEEE PES PowerTech Conference: Towards and Beyond Sustainable Energy Systems - Manchester, United Kingdom
Duration: 18 Jun 201722 Jun 2017

Conference

Conference12th IEEE PES PowerTech Conference
CountryUnited Kingdom
CityManchester
Period18/06/1722/06/17

Keywords

  • wind power forecasting
  • big data
  • machine learning
  • LASSO
  • gradient boosting
  • wind farms
  • weather forecasting

Fingerprint Dive into the research topics of 'Use of turbine-level data for improved wind power forecasting'. Together they form a unique fingerprint.

Cite this