Improving forecast accuracy using a synthetic weather station: an incremental approach and BFCom2018 lessons learned

Daniel Donaldson, Zafar Khan, Dilan Jayaweera

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

1 Citation (Scopus)
273 Downloads (Pure)


Selection of the most appropriate forecast model should be governed by the underlying data. This paper investigates the impact of benchmark model selection, recency effect and synthetic weather station selection techniques on load forecast performance and presents a new weighted average based approach to generate a synthetic weather station. Lessons learned from this effort include the criticality of using benchmark models, the need for additional public datasets, and the value of forecasting competitions for learning and model development. The results from this case study validate that addition of recency effect and use of a synthetic weather station, can provide substantial forecast improvements over benchmark models. The results also demonstrate the potential benefit of using a weighted approach for synthetic weather station generation rather than simple averaging.
Original languageEnglish
Title of host publication2019 IEEE Milano Powertech
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages6
ISBN (Electronic)978-1-5386-4722-6
ISBN (Print) 978-1-5386-4723-3 (PoD)
Publication statusPublished - 26 Aug 2019
EventIEEE PowerTech 2019, 13th - Bovisa Campus of Politecnico di Milano, Milano, Italy
Duration: 23 Jun 201927 Jun 2019

Publication series

NameIEEE Milan PowerTech


ConferenceIEEE PowerTech 2019, 13th


  • Load modeling
  • Benchmark testing
  • Predictive models
  • Weather forecasting
  • Mathematical model
  • Data models
  • Load forecasting
  • Multiple linear regression
  • Weather station combination


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