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Eine Liste aller Seiten, die das Attribut „Kurzfassung“ mit dem Wert „Non-intrusive load monitoring (NILM) algorithms aim at disaggregating consumption curves of households to the level of single appliances. However, there is no conventional way of quantifying and representing the tradeoff between the quality of analyses, such as the accuracy of the disaggregated consumption curves, and the load on the available computing resources. Thus, it is hard to plan the underlying infrastructure and resources for the analysis system and to find the optimal configuration of the system. This thesis introduces a system that assesses the quality of different analyses and their runtime behavior. This assessment is done based on varying configuration parameters and changed characteristics of the input dataset. Varied characteristics are the granularity of the data and the noisiness of the data. We demonstrate that the collected runtime behavior data can be used to choose reasonable characteristics of the input data set.“ haben. Weil nur wenige Ergebnisse gefunden wurden, werden auch ähnliche Werte aufgelistet.

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Liste der Ergebnisse

    • Analysis of Classifier Performance on Aggregated Energy Status Data  + (Non-intrusive load monitoring (NILM) algorNon-intrusive load monitoring (NILM) algorithms aim at disaggregating consumption curves of households to the level of single appliances. However, there is no conventional way of quantifying and representing the tradeoff between the quality of analyses, such as the accuracy of the disaggregated consumption curves, and the load on the available computing resources. Thus, it is hard to plan the underlying infrastructure and resources for the analysis system and to find the optimal configuration of the system. This thesis introduces a system that assesses the quality of different analyses and their runtime behavior. This assessment is done based on varying configuration parameters and changed characteristics of the input dataset. Varied characteristics are the granularity of the data and the noisiness of the data. We demonstrate that the collected runtime behavior data can be used to choose reasonable characteristics of the input data set.ble characteristics of the input data set.)