Missing data in wind farm time series: properties and effect on forecasts

Rosemary Tawn, Jethro Browell, Iain Dinwoodie

Research output: Contribution to journalArticlepeer-review

Abstract

Missing or corrupt data is common in real-world datasets; this affects the estimation and operation of analytical models where completeness is assumed or required. Statistical wind power forecasts utilise recent turbine data as model inputs, and must therefore be robust to missing data. We find that wind power data is ‘missing not at random’, with missing patterns also related to the forecast output. Approaches for dealing with this missing data in training and operation are proposed and evaluated through a case study, leading to a suggested forecasting methodology in the presence of missing data. In the training set, missing data was found to have significant negative impact on performance if simply omitted but this can be almost completely mitigated using multiple imputation. Greater increase in forecast errors is seen when input data are missing operationally, and retraining forecast models using the remaining inputs is found to be preferable to imputation.
Original languageEnglish
Article number106640
JournalElectric Power Systems Research
Volume189
Early online date11 Aug 2020
DOIs
Publication statusPublished - 31 Dec 2020
EventXXI Power Systems Computation Conference - Faculty of Engineering, University of Porto, Porto, Portugal
Duration: 29 Jun 20203 Jul 2020
Conference number: 21
https://pscc2020.pt/

Keywords

  • forecasting
  • missing data
  • time series
  • vector autoregression
  • wind power

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