Thole Klingenberg

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In this paper we employ smart meter and support vector machines (SVM) for the problem of recognizing household appliances’ load patterns in measured load time series, which is an important step for various applications in energy consulting, process recognition or health care applications. We present an automated data collection and preprocessing approach(More)
This paper proposes a novel dynamic design for control reserve dimensioning. In contrast to the current statistical analytic design we present a data driven approach with methods of computational intelligence. The chosen k-nearest neighbor algorithm is one of the most sucessfully used methods in machine learning. The model is able to predict complex(More)
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