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Feature selection in short-term load forecasting
KTH, School of Electrical Engineering and Computer Science (EECS).
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Val av attribut vid kortvarig lastprognos för energiförbrukning (Swedish)
Abstract [en]

This paper investigates correlation between energy consumption 24 hours ahead and features used for predicting energy consumption. The features originate from three categories: weather, time and previous energy. The correlations are calculated using Pearson correlation and mutual information. This resulted in the highest correlated features being those representing previous energy consumption, followed by temperature and month.

Two identical feature sets containing all attributes1 were obtained by ranking the features according to correlation. Three feature sets were created manually. The first set contained seven attributes representing previous energy consumption over the course of seven days prior to the day of prediction. The second set consisted of weather and time attributes. The third set consisted of all attributes from the first and second set. These sets were then compared on different machine learning models. It was found the set containing all attributes and the set containing previous energy attributes yielded the best performance for each machine learning model.

1In this report, the words ”attribute” and ”feature” are used interchangeably.

Abstract [sv]

I denna rapport undersöks korrelation och betydelsen av olika attribut för att förutspå energiförbrukning 24 timmar framåt. Attributen härstammar från tre kategorier: väder, tid och tidigare energiförbrukning. Korrelationerna tas fram genom att utföra Pearson Correlation och Mutual Information. Detta resulterade i att de högst korrelerade attributen var de som representerar tidigare energiförbrukning, följt av temperatur och månad.

Två identiska attributmängder erhölls genom att ranka attributen över korrelation. Tre attributmängder skapades manuellt. Den första mängden innehåll sju attribut som representerade tidigare energiförbrukning, en för varje dag, sju dagar innan datumet för prognosen av energiförbrukning. Den andra mängden bestod av väderoch tidsattribut. Den tredje mängden bestod av alla attribut från den första och andra mängden. Dessa mängder jämfördes sedan med hjälp av olika maskininlärningsmodeller. Resultaten visade att mängden med alla attribut och den med tidigare energiförbrukning gav bäst resultat för samtliga modeller.

Place, publisher, year, edition, pages
2019. , p. 31
Series
TRITA-EECS-EX ; 2019:350
Keywords [en]
Short-term load forecasting, energy consumption forecasting, Linear regression, SVR, Random Forest, machine learning, regression, feature selection, attribute selection, Pearson correlation, Mutual information, correlation matrix, Two-way ANOVA, Tukey’s HSD test.
Keywords [sv]
Kortsiktig lastprognos, Energiförbrukningsprognos, Linjär regression, SVR, Random forest, Maskininlärning, Attributval, Pearson-korrelation, Ömsesidig information, Korrelationsmatris, Tvåvägs ANOVA, Tukey’s HSD-test.
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-259692OAI: oai:DiVA.org:kth-259692DiVA, id: diva2:1353001
Supervisors
Examiners
Available from: 2019-09-24 Created: 2019-09-20 Last updated: 2019-09-24Bibliographically approved

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