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  • 1.
    Bjurén, Johan
    University of Skövde, School of Humanities and Informatics.
    Från modell till modell: Transformering av information från ett modellspråk till ett annat2011Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE creditsStudent thesis
    Abstract [sv]

    Detta arbete undersöker möjligheterna att ta fram riktlinjer för en transformation från modelltyperna i EKD, som är ett mindre formellt semi-formellt modelleringsspråk, till UML, som är ett mer formellt semi-formellt modelleringsspråk, i syfte att komma närmare en systemspecifikation som kan implementeras som ett IT-system. Arbetet är utfört med en designansats som grund för hur arbetsprocessen sett ut. Processen följer genom hela arbetet och kan läsas om under metodkapitlet. Arbetet tar upp frågor som vilka modelltyper som är lämpliga att transformera till, vilken information som kan transformeras, vilken information som inte kan transformeras och vad som kan göras med denna. Riktlinjerna testas i valideringssyfte även på EKD modeller som skapats i annat syfte än för denna rapport.

  • 2.
    Bjurén, Johan
    University of Skövde, School of Informatics.
    USING CASE-BASED REASONING FOR PREDICTING ENERGY USAGE2013Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    In this study, the inability to in a future meet the electricity demand and the urge to change the consumption behavior considered. In a smart grid context there are several possible ways to do this. Means include ways to increase the consumer’s awareness, add energy storages or build smarter homes which can control the appliances. To be able to implement these, indications on how the future consumption will be could be useful. Therefore we look further into how a framework for short-term consumption predictions can be created using electricity consumption data in relation to external factors. To do this a literature study is made to see what kind of methods that are relevant and which qualities is interesting to look at in order to choose a good prediction method. Case Based Reasoning seemed to be able to be suitable method. This method was examined further and built using relational databases. After this the method was tested and evaluated using datasets and evaluation methods CV, MBE and MAPE, which have previously been used in the domain of consumption prediction. The result was compared to the results of the winning methods in the ASHRAE competition. The CBR method was expected to perform better than what it did, and still not as good as the winning methods from the ASHRAE competition. The result showed that the CBR method can be used as a predictor and has potential to make good energy consumption predictions. and there is room for improvement in future studies.

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