clustering is one of the most important data analysis tasks. It is used to organize data points into groups or clusters. Each cluster has similar instances, which are dissimilar to instances belonging to other cluster...
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Load forecasting is one of the critical activities in electric power system planning. This paper presents clustering algorithms and their usage in load forecasting on a case study in Zagreb, Croatia. Load data acquisi...
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The interaction between electric vehicles (EV) and the future energy system is subject of current research in the field of energy system analysis. EVs represent an additional electrical load on the one hand and a pote...
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ISBN:
(纸本)9781839536793
The interaction between electric vehicles (EV) and the future energy system is subject of current research in the field of energy system analysis. EVs represent an additional electrical load on the one hand and a potential flexibility provider through smart charging on the other. Feedback effects on the energy system and potential benefits of intelligently charged EVs depend on a variety of technical parameters as well as the individual driving behavior of vehicle owners. Since no sufficient data on EV users driving behavior is currently available, synthetic profiles have to be used. In this paper we propose a methodological approach that combines the mobility data of the two main household travel surveys in Germany - the Mobility in Germany 2017 and the German Mobility panel - to synthesize annual mobility profiles that represent the German mobility behavior. To guarantee statistical soundness, the methodology requires a large number of individual profiles used for further evaluations. Computational power however limits the maximum number of usable profiles. In the context of this paper, we assess and compare potential revenues of a price optimized unidirectional and bidirectional charging strategy. Those evaluations are carried out for 10,000 profiles with the linear optimization model eFLAME. Resulting revenues and vehicle-specific indicators such as equivalent full cycles (EFC) and charging/discharging hours serve as a reference for further evaluations with a reduced number of profiles. To reduce that number, we compare two distinct methodological approaches. The first approach is based on randomly drawing an increasing number of profiles, while the second is based on applying various clustering algorithms to specifically identify representative profiles. In the context of clustering algorithms, we test and compare distinct feature definitions, preanalysis methods and include a principal component analysis (PCA) to identify the best cluster of representative pro
The topology of the distributed cognitive radio network is volatile as influenced by the behavior of primary users, and this condition leads to the large communication overhead and low utilization of spectrum resource...
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In wireless self-organized network (WSON), AOW clustering algorithms can achieve better performance and high adaptability, but having limitations in certain circumstances. In this paper, working procedure and shortcom...
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High dimensional data analysis poses some interesting and counter intuitive problems. One of this problems is, that some clustering algorithms do not work or work only very poorly if the dimensionality of the feature ...
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This paper presents a new approach designed to reduce the computational load of the existing clustering algorithms by trimming down the documents size using fingerprinting methods. Thorough evaluation was performed ov...
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With the ever increasing data, there is a greater need for analyzing and extracting useful and meaningful information out of it. The amount of research being conducted in extracting this information is commendable. Fr...
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clustering a document collection is the current approach to automatically derive underlying document categories. The categorization performance of a document clustering algorithm can be captured by the F-Measure, whic...
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This paper performs a clustering algorithm for portfolio investment diversification. The clustering process is applied to choose the preferred assets among hundreds of assets provided in the market under the related f...
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