Definition of MV Load Diagrams via Weighted Evidence Accumulation Clustering using Subsampling

A definition of medium voltage (MV) load diagrams was made, based on the data base knowledge discovery process. Clustering techniques were used as support for the agents of the electric power retail markets to obtain specific knowledge of their customers’ consumption habits. Each customer class resulting from the clustering operation is represented by its load diagram. The Two-step clustering algorithm and the WEACS approach based on evidence accumulation (EAC) were applied to an electricity consumption data from a utility client’s database in order to form the customer’s classes and to find a set of representative consumption patterns. The WEACS approach is a clustering ensemble combination approach that uses subsampling and that weights differently the partitions in the co-association matrix. As a complementary step to the WEACS approach, all the final data partitions produced by the different variations of the method are combined and the Ward Link algorithm is used to obtain the final data partition. Experiment results showed that WEACS approach led to better accuracy than many other clustering approaches. In this paper the WEACS approach separates better the customer’s population than Two-step clustering algorithm.

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.

A probabilistic methodology for distributed generation location in isolated electrical service area

Distributed generation unlike centralized electrical generation aims to generate electrical energy on small scale as near as possible to load centers, interchanging electric power with the network. This work presents a probabilistic methodology conceived to assist the electric system planning engineers in the selection of the distributed generation location, taking into account the hourly load changes or the daily load cycle. The hourly load centers, for each of the different hourly load scenarios, are calculated deterministically. These location points, properly weighted according to their load magnitude, are used to calculate the best fit probability distribution. This distribution is used to determine the maximum likelihood perimeter of the area where each source distributed generation point should preferably be located by the planning engineers. This takes into account, for example, the availability and the cost of the land lots, which are factors of special relevance in urban areas, as well as several obstacles important for the final selection of the candidates of the distributed generation points. The proposed methodology has been applied to a real case, assuming three different bivariate probability distributions: the Gaussian distribution, a bivariate version of Freund’s exponential distribution and the Weibull probability distribution. The methodology algorithm has been programmed in MATLAB. Results are presented and discussed for the application of the methodology to a realistic case and demonstrate the ability of the proposed methodology for efficiently handling the determination of the best location of the distributed generation and their corresponding distribution networks.

Ant colony system based approach to single machine scheduling problems: weighted tardiness scheduling problem

The paper introduces an approach to solve the problem of generating a sequence of jobs that minimizes the total weighted tardiness for a set of jobs to be processed in a single machine. An Ant Colony System based algorithm is validated with benchmark problems available in the OR library. The obtained results were compared with the best available results and were found to be nearer to the optimal. The obtained computational results allowed concluding on their efficiency and effectiveness.

Polycyclic aromatic hydrocarbons in gas and particulate phases of indoor environments influenced by tobacco smoke: levels, phase distributions, and health risks

As polycyclic aromatic hydrocarbons (PAHs) have a negative impact on human health due to their mutagenic and/or carcinogenic properties, the objective of this work was to study the influence of tobacco smoke on levels and phase distribution of PAHs and to evaluate the associated health risks. The air samples were collected at two homes; 18 PAHs (the 16 PAHs considered by U.S. EPA as priority pollutants, dibenzo[a,l]pyrene and benzo[j]fluoranthene) were determined in gas phase and associated with thoracic (PM10) and respirable (PM2.5) particles. At home influenced by tobacco smoke the total concentrations of 18 PAHs in air ranged from 28.3 to 106 ngm 3 (mean of 66.7 25.4 ngm 3),∑PAHs being 95% higher than at the non-smoking one where the values ranged from 17.9 to 62.0 ngm 3 (mean of 34.5 16.5 ngm 3). On average 74% and 78% of ∑PAHs were present in gas phase at the smoking and non-smoking homes, respectively, demonstrating that adequate assessment of PAHs in air requires evaluation of PAHs in both gas and particulate phases. When influenced by tobacco smoke the health risks values were 3.5e3.6 times higher due to the exposure of PM10. The values of lifetime lung cancer risks were 4.1 10 3 and 1.7 10 3 for the smoking and nonsmoking homes, considerably exceeding the health-based guideline level at both homes also due to the contribution of outdoor traffic emissions. The results showed that evaluation of benzo[a]pyrene alone would probably underestimate the carcinogenic potential of the studied PAH mixtures; in total ten carcinogenic PAHs represented 36% and 32% of the gaseous ∑PAHs and in particulate phase they accounted for 75% and 71% of ∑PAHs at the smoking and non-smoking homes, respectively.

Quantification of endocrine disruptors and pesticides in water by gas chromatography–tandem mass spectrometry. Method validation using weighted linear regression schemes

Amulti-residue methodology based on a solid phase extraction followed by gas chromatography–tandem mass spectrometry was developed for trace analysis of 32 compounds in water matrices, including estrogens and several pesticides from different chemical families, some of them with endocrine disrupting properties. Matrix standard calibration solutions were prepared by adding known amounts of the analytes to a residue-free sample to compensate matrix-induced chromatographic response enhancement observed for certain pesticides. Validation was done mainly according to the International Conference on Harmonisation recommendations, as well as some European and American validation guidelines with specifications for pesticides analysis and/or GC–MS methodology. As the assumption of homoscedasticity was not met for analytical data, weighted least squares linear regression procedure was applied as a simple and effective way to counteract the greater influence of the greater concentrations on the fitted regression line, improving accuracy at the lower end of the calibration curve. The method was considered validated for 31 compounds after consistent evaluation of the key analytical parameters: specificity, linearity, limit of detection and quantification, range, precision, accuracy, extraction efficiency, stability and robustness.

A Review on the Characterization of Signals and Systems by Power Law Distributions

Power laws, also known as Pareto-like laws or Zipf-like laws, are commonly used to explain a variety of real world distinct phenomena, often described merely by the produced signals. In this paper, we study twelve cases, namely worldwide technological accidents, the annual revenue of America׳s largest private companies, the number of inhabitants in America׳s largest cities, the magnitude of earthquakes with minimum moment magnitude equal to 4, the total burned area in forest fires occurred in Portugal, the net worth of the richer people in America, the frequency of occurrence of words in the novel Ulysses, by James Joyce, the total number of deaths in worldwide terrorist attacks, the number of linking root domains of the top internet domains, the number of linking root domains of the top internet pages, the total number of human victims of tornadoes occurred in the U.S., and the number of inhabitants in the 60 most populated countries. The results demonstrate the emergence of statistical characteristics, very close to a power law behavior. Furthermore, the parametric characterization reveals complex relationships present at higher level of description.

Polycyclic aromatic hydrocarbons: levels and phase distributions in preschool microenvironment

This work aims to characterize levels and phase distribution of polycyclic aromatic hydrocarbons (PAHs) in indoor air of preschool environment and to assess the impact of outdoor PAH emissions to indoor environment. Gaseous and particulate (PM1 and PM2.5) PAHs (16 USEPA priority pollutants, plus dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were concurrently sampled indoors and outdoors in one urban preschool located in north of Portugal for 35 days. The total concentration of 18 PAHs (ΣPAHs) in indoor air ranged from 19.5 to 82.0 ng/m3; gaseous compounds (range of 14.1–66.1 ng/m3) accounted for 85% ΣPAHs. Particulate PAHs (range 0.7–15.9 ng/m3) were predominantly associated with PM1 (76% particulate ΣPAHs) with 5-ring PAHs being the most abundant. Mean indoor/outdoor ratios (I/O) of individual PAHs indicated that outdoor emissions significantly contributed to PAH indoors; emissions from motor vehicles and fuel burning were the major sources.