Determination of electricity consumers’ load profiles via weighted evidence accumulation clustering using subsampling

With the electricity market liberalization, the distribution and retail companies are looking for better market strategies based on adequate information upon the consumption patterns of its electricity consumers. A fair insight on the consumers’ behavior will permit the definition of specific contract aspects based on the different consumption patterns. In order to form the different consumers’ classes, and find a set of representative consumption patterns we use electricity consumption data from a utility client’s database and two approaches: Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) for combining partitions in a clustering ensemble. While EAC uses a voting mechanism to produce a co-association matrix based on the pairwise associations obtained from N partitions and where each partition has equal weight in the combination process, the WEACS approach uses subsampling and weights differently the partitions. As a complementary step to the WEACS approach, we combine the partitions obtained in the WEACS approach with the ALL clustering ensemble construction method and we use the Ward Link algorithm to obtain the final data partition. The characterization of the obtained consumers’ clusters was performed using the C5.0 classification algorithm. Experiment results showed that the WEACS approach leads to better results than many other clustering approaches.

Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

On clustering interval data with different scales of measures : experimental results

This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also acknowledge & be non-commercial.

Clustering of variables with a three-way approach for health sciences

TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.

Clustering an interval data set : are the main partitions similar to a priori partition?

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Pulmonary tuberculosis in Continental Portugal 2000-2010: temporal trends clustering as a tool for control evaluation

Clustering analysis is a useful tool to detect and monitor disease patterns and, consequently, to contribute for an effective population disease management. Portugal has the highest incidence of tuberculosis in the European Union (in 2012, 21.6 cases per 100.000 inhabitants), although it has been decreasing consistently. Two critical PTB (Pulmonary Tuberculosis) areas, metropolitan Oporto and metropolitan Lisbon regions, were previously identified through spatial and space-time clustering for PTB incidence rate and risk factors. Identifying clusters of temporal trends can further elucidate policy makers about municipalities showing a faster or a slower TB control improvement.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

Legal and political regulation of higher education in Portugal: an evolutionary perspective on the current policy issues and the problem of order

The higher education system in Europe is currently under stress and the debates over its reform and future are gaining momentum. Now that, for most countries, we are in a time for change, in the overall society and the whole education system, the legal and political dimensions have gained prominence, which has not been followed by a more integrative approach of the problem of order, its reform and the issue of regulation, beyond the typical static and classical cost-benefit analyses. The two classical approaches for studying (and for designing the policy measures of) the problem of the reform of the higher education system - the cost-benefit analysis and the legal scholarship description - have to be integrated. This is the argument of our paper that the very integration of economic and legal approaches, what Warren Samuels called the legal-economic nexus, is meaningful and necessary, especially if we want to address the problem of order (as formulated by Joseph Spengler) and the overall regulation of the system. On the one hand, and without neglecting the interest and insights gained from the cost-benefit analysis, or other approaches of value for money assessment, we will focus our study on the legal, social and political aspects of the regulation of the higher education system and its reform in Portugal. On the other hand, the economic and financial problems have to be taken into account, but in a more inclusive way with regard to the indirect and other socio-economic costs not contemplated in traditional or standard assessments of policies for the tertiary education sector. In the first section of the paper, we will discuss the theoretical and conceptual underpinning of our analysis, focusing on the evolutionary approach, the role of critical institutions, the legal-economic nexus and the problem of order. All these elements are related to the institutional tradition, from Veblen and Commons to Spengler and Samuels. The second section states the problem of regulation in the higher education system and the issue of policy formulation for tackling the problem. The current situation is clearly one of crisis with the expansion of the cohorts of young students coming to an end and the recurrent scandals in private institutions. In the last decade, after a protracted period of extension or expansion of the system, i. e., the continuous growth of students, universities and other institutions are competing harder to gain students and have seen their financial situation at risk. It seems that we are entering a period of radical uncertainty, higher competition and a new configuration that is slowly building up is the growth in intensity, which means upgrading the quality of the higher learning and getting more involvement in vocational training and life-long learning. With this change, and along with other deep ones in the Portuguese society and economy, the current regulation has shown signs of maladjustment. The third section consists of our conclusions on the current issue of regulation and policy challenge. First, we underline the importance of an evolutionary approach to a process of change that is essentially dynamic. A special attention will be given to the issues related to an evolutionary construe of policy analysis and formulation. Second, the integration of law and economics, through the notion of legal economic nexus, allows us to better define the issues of regulation and the concrete problems that the universities are facing. One aspect is the instability of the political measures regarding the public administration and on which the higher education system depends financially, legally and institutionally, to say the least. A corollary is the lack of clear strategy in the policy reforms. Third, our research criticizes several studies, such as the one made by the OECD in late 2006 for the Ministry of Science, Technology and Higher Education, for being too static and neglecting fundamental aspects of regulation such as the logic of actors, groups and organizations who are major players in the system. Finally, simply changing the legal rules will not necessary per se change the behaviors that the authorities want to change. By this, we mean that it is not only remiss of the policy maker to ignore some of the critical issues of regulation, namely the continuous non-respect by academic management and administrative bodies of universities of the legal rules that were once promulgated. Changing the rules does not change the problem, especially without the necessary debates form the different relevant quarters that make up the higher education system. The issues of social interaction remain as intact. Our treatment of the matter will be organized in the following way. In the first section, the theoretical principles are developed in order to be able to study more adequately the higher education transformation with a modest evolutionary theory and a legal and economic nexus of the interactions of the system and the policy challenges. After describing, in the second section, the recent evolution and current working of the higher education in Portugal, we will analyze the legal framework and the current regulatory practices and problems in light of the theoretical framework adopted. We will end with some conclusions on the current problems of regulation and the policy measures that are discusses in recent years.

CIDER - envisaging a COTS communication infrastructure for evolutionary dependable real-time systems

It is foreseen that future dependable real-time systems will also have to meet flexibility, adaptability and reconfigurability requirements. Considering the distributed nature of these computing systems, a communication infrastructure that permits to fulfil all those requirements is thus of major importance. Although Ethernet has been used primarily as an information network, there is a strong belief that some very recent technological advances will enable its use in dependable applications with real-time requirements. Indeed, several recently standardised mechanisms associated with Switched-Ethernet seem to be promising to enable communication infrastructures to support hard real-time, reliability and flexible distributed applications. This paper describes the motivation and the work being developed within the CIDER (Communication Infrastructure for Dependable Evolvable Real-Time Systems) project, which envisages the use of COTS Ethernet as an enabling technology for future dependable real-time systems. It is foreseen that the CIDER approach will constitute a relevant stream of research since it will bring together cutting edge research in the field of real-time and dependable distributed systems and the industrial eagerness to expand Ethernet responsabilities to support dependable real-time applications.

Fuzzy clustering applied to a demand response model in a smart grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.

ECG Analysis Using Consensus Clustering

Biosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.

Fuzzy clustering applied to a demand response model in a smatr grid contingency scenario

This paper focus on a demand response model analysis in a smart grid context considering a contingency scenario. A fuzzy clustering technique is applied on the developed demand response model and an analysis is performed for the contingency scenario. Model considerations and architecture are described. The demand response developed model aims to support consumers decisions regarding their consumption needs and possible economic benefits.