Clustering Techniques in Load Characteristic Analysis
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Today, load characteristic analysis plays a vital role in network planning, operation and control. In particular, with massive demand side participation activities on the network, the characteristics of individual load determines the way of the active load management as well as the network pricing strategies. In this paper, the load characteristics are analyzed utilizing clustering techniques for a tropical isle with massive temperature sensitive loads. Through deeply mining of real data, the features of individual loads were observed to define the way of load participation.Keywords:
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Distance choice is an important issue in power load pattern extraction using clustering techniques, so it is necessary to find the influence on clustering result of load curves using different distances in clustering algorithms. In this paper several distances are used in the k-means algorithm for clustering load curves and their influences on the clustering results are analyzed, therefore, the suitable distance for the k-means algorithms is obtained. An example with 147 electricity customers load curves shows distances have different influences on clustering results using the same clustering algorithm. The comparison results indicate that the choice of distances is an important issue in power load pattern extraction using clustering techniques and a suitable distance may improve the accuracy of mining algorithms.
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Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and practical implementations of such tasks, in particular of clustering, is very rudimentary. This note focuses on clustering. I claim that the most signif- icant challenge for clustering is model selection. In contrast with other common computational tasks, for clustering, dif- ferent algorithms often yield drastically different outcomes. Therefore, the choice of a clustering algorithm, and their pa- rameters (like the number of clusters) may play a crucial role in the usefulness of an output clustering solution. However, currently there exists no methodical guidance for clustering tool-selection for a given clustering task. Practitioners pick the algorithms they use without awareness to the implications of their choices and the vast majority of theory of clustering papers focus on providing savings to the resources needed to solve optimization problems that arise from picking some concrete clustering objective. Saving that pale in com- parison to the costs of mismatch between those objectives and the intended use of clustering results. I argue the severity of this problem and describe some recent proposals aiming to address this crucial lacuna.
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In modern days cloud computing is one of the greatest platform which provides storage of data in very lower cost and available for all time over the internet. But the cloud computing has more critical issue like security, load balancing and fault tolerance ability. In this paper we are focusing on Load Balancing approach. The Load balancing is the process of distributing load over the different nodes which provides good resource utilization when nodes are overloaded with job. Load balancing is required to handle the load when one node is overloaded. When the node is overloaded at that time load is distributed over the other ideal nodes. Many load balancing algorithms are available for load balancing like Static load balancing and Dynamic load balancing. The survey of modern load balancing algorithm is presented in this paper. The Load balancing is the process of distributing load over the different nodes which provides good resource utilization when nodes are overloaded with job. Load balancing is required to handle the load when one node is overloaded. When the node is overloaded at that time load is distributed over the other ideal nodes. Many load balancing algorithms are available for load balancing like Static load balancing and Dynamic load balancing.
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Unsupervised learning is widely recognized as one of the most important challenges facing machine learning nowa- days. However, in spite of hundreds of papers on the topic being published every year, current theoretical understanding and practical implementations of such tasks, in particular of clustering, is very rudimentary. This note focuses on clustering. I claim that the most signif- icant challenge for clustering is model selection. In contrast with other common computational tasks, for clustering, dif- ferent algorithms often yield drastically different outcomes. Therefore, the choice of a clustering algorithm, and their pa- rameters (like the number of clusters) may play a crucial role in the usefulness of an output clustering solution. However, currently there exists no methodical guidance for clustering tool-selection for a given clustering task. Practitioners pick the algorithms they use without awareness to the implications of their choices and the vast majority of theory of clustering papers focus on providing savings to the resources needed to solve optimization problems that arise from picking some concrete clustering objective. Saving that pale in com- parison to the costs of mismatch between those objectives and the intended use of clustering results. I argue the severity of this problem and describe some recent proposals aiming to address this crucial lacuna.
Conceptual clustering
Consensus clustering
Constrained clustering
Implementation
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