A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.

Clustering of Territorial Areas: A Multicriteria Districting problem

The success of regional development policies depends on the homogeneity of the territorial units. This paper aims to propose a framework for obtaining homogenous territorial clusters based on a Pareto frontier considering multiple criteria related to territories’ endogenous resources, economic profile and socio-cultural features. This framework is developed in two phases. First, the criteria correlated with development at the territorial unit level are determined through statistical and econometric methods. Then, a multi-criteria approach is developed to allocate each territorial unit (parishes) to a territorial agglomerate, according to the Pareto frontier established.

A fuzzy clustering approach to a demand response model

This paper proposes a novel demand response model using a fuzzy subtractive cluster approach. The model development provides support to domestic consumer decisions on controllable loads management, considering consumers’ consumption needs and the appropriate load shape or rescheduling in order to achieve possible economic benefits. The model based on fuzzy subtractive clustering method considers clusters of domestic consumption covering an adequate consumption range. Analysis of different scenarios is presented considering available electric power and electric energy prices. Simulation results are presented and conclusions of the proposed demand response model are discussed.