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Electrical power demand forecasting
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The electrical load, sampled every hour, at Salagatan 18 in Uppsala was used to form models and for forecasting the load. It was investigated whether Multi Seasonal ARIMA models and Support Vector Regression were suitable. The models were compared to a naive persistence benchmark in periods of high and low volatility. Both short range 24h and long range 168h forecasts were made. It was concluded that both model proposals could be used to forecast the electrical load series. ARIMA and Support Vector Regression model proposals outperformed the benchmark for long and short range forecasts in both volatile and non volatile settings. The mean absolute percentage errors of the best ARIMA model for a one week forecast were 15.1% and 21.6% for non volatile and volatile settings, respectively.

Place, publisher, year, edition, pages
2019.
Series
TVE-E ; 19006
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-385594OAI: oai:DiVA.org:uu-385594DiVA, id: diva2:1325158
Educational program
Master Programme in Electrical Engineering
Supervisors
Examiners
Available from: 2019-06-17 Created: 2019-06-14 Last updated: 2019-06-17Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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