In the electricity market, it is highly desirable for suppliers to know the electricity consumption behavior of their customers, in order to provide them with satisfactory services with the minimum cost. Information o...
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ISBN:
(纸本)9781424465477
In the electricity market, it is highly desirable for suppliers to know the electricity consumption behavior of their customers, in order to provide them with satisfactory services with the minimum cost. Information on customers' consumption pattern in the deregulated power system is becoming critical for distribution companies. One of the suitable tools for extracting characteristics of customers is the clustering technique. Selection of better methods among several existing clustering methods should be considered. Therefore, in this paper, we evaluate the performance of Classical K-Means, Weighted Fuzzy Average K-Means, Modified Follow the Leader, Self-Organizing Maps and hierarchical algorithms that are more applicable in clustering load curves. The performances were compared by using two adequacy measures named Clustering Dispersion Indicator and Mean Index Adequacy.
The paper gives a general framework which allows to study various hierarchical structures of an organisation. It provides a unified view on hierarchical algorithms, hierarchical planning, principal-agent relationships...
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The paper gives a general framework which allows to study various hierarchical structures of an organisation. It provides a unified view on hierarchical algorithms, hierarchical planning, principal-agent relationships, and hierarchical negotiations. The main idea is to describe an organisation as a superposition of interfering individual decision processes resulting in a pair of functional equations. Different specifications of these equations allow to characterize the above mentioned hierarchical phenomena. Since management can be considered as a planning and leadership activity to control the interrelationship of individual decision processes, the paper gives a contribution to a better understanding of the general structure of the management process.
In this paper, we propose an O(N log N) hierarchical random compression method (HRCM) for kernel matrix compressing, which only requires sampling O(N log N) entries of a matrix. The HRCM combines the hierarchical fram...
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In this paper, we propose an O(N log N) hierarchical random compression method (HRCM) for kernel matrix compressing, which only requires sampling O(N log N) entries of a matrix. The HRCM combines the hierarchical framework of the H-matrix and a randomized sampling technique of column and row spaces for far-field interaction kernel matrix. We show that a uniform column/row sampling of a far-field kernel matrix, thus without the need and associated cost to pre-compute a costly sampling distribution, will give a low-rank compression of such low-rank matrix, independent of the matrix size and only dependent on the separation of the source and target locations. This far-field random compression technique is then implemented at each level of the hierarchical decomposition for general kernel matrices, resulting in an O(N log N) random compression method. Error and complexity analysis for the HRCM are included. Numerical results for electrostatic and low frequency Helmholtz wave kernels have validated the efficiency and accuracy of the proposed method in comparison of direct O(N-2) summations. (C) 2019 Elsevier Inc. All rights reserved.
This study employs clustering analysis to group forest management units using auxiliary, satellite imagery-derived height metrics and past wall-to-wall tree census data from a natural, uneven-aged forest. Initially, w...
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This study employs clustering analysis to group forest management units using auxiliary, satellite imagery-derived height metrics and past wall-to-wall tree census data from a natural, uneven-aged forest. Initially, we conducted an exhaustive exploration to determine the optimal number of clusters k, considering a wide range of clustering schemes, indices, and two specific k ranges. The optimal k is influenced by various factors, including the minimum k considered, the selected clustering algorithm, the clustering indices used, and the auxiliary variables. Specifically, the minimum k, the Euclidean distance metric, and the clustering index were instrumental in determining the optimal cluster numbers, with algorithms exerting minimal influence. Unlike traditional validation indices, we assessed the performance of these optimally defined clusters based on direct estimates and additional criteria. Subsequently, our research introduces a twofold methodology for Small Area Estimation (SAE). The first approach focuses on aggregating forest management units at the cluster level to increase the sample size, thereby yielding reliable design-based direct estimates for key forest attributes, including growing stock volume, basal area, tree density, and mean tree height. The second approach prepares area-level data for the future application of model-based estimators, contingent on establishing a strong correlation between target and auxiliary variables. Our methodology has the potential to enhance forest inventory practices across a wide range of forests where area-level auxiliary covariates are available.
In the electricity market, it is highly desirable for suppliers to know the electricity consumption behavior of their customers, in order to provide them with satisfactory services with the minimum cost. Information o...
详细信息
ISBN:
(纸本)9781424465460
In the electricity market, it is highly desirable for suppliers to know the electricity consumption behavior of their customers, in order to provide them with satisfactory services with the minimum cost. Information on customers' consumption pattern in the deregulated power system is becoming critical for distribution companies. One of the suitable tools for extracting characteristics of customers is the clustering technique. Selection of better methods among several existing clustering methods should be considered. Therefore, in this paper, we evaluate the performance of Classical K-Means, Weighted Fuzzy Average K-Means, Modified Follow the Leader, Self-Organizing Maps and hierarchical algorithms that are more applicable in clustering load curves. The performances were compared by using two adequacy measures named Clustering Dispersion Indicator and Mean Index Adequacy.
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