This work has as objectives the implementation of a intelligent computational tool to identify the non-technical losses and to select its most relevant features, considering information from the database with industrial consumers profiles of a power company. The solution to this problem is not trivial and not of regional character, the minimization of non-technical loss represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. This work presents using the WEKA software to the proposed objective, comparing various classification techniques and optimization through intelligent algorithms, this way, can be possible to automate applications on Smart Grids.
Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based Optimum Path Forest (OPF) classifier to handle with binary classification problems, and we show it can be more accurate than naïve OPF in a number of datasets. In addition to being just more accurate or not, probabilistic OPF turns to be another useful tool to the scientific community.
Gradually smart grids and smart meters are closer to the home consumers. Several countries has developed studies focused in the impacts arising from the introduction of these technologies and one of the main advantages are related to energy efficiency, observed through the awareness of the population on behalf of a more efficient consumption. These benefits are felt directly by consumers through the savings on electricity bills and also by the concessionaires through the minimization of losses in transmission and distribution, system stability, smaller loading during peak hours, among others. In this article two projects that demonstrate the potential energy savings through smart meters and smart grids are presented. The first performed in Korea, focusing on the installation of smart meters and the impact of use of user interfaces. The second performed in Portugal, focusing on the control of loads in a residence with distributed generation.
Machine learning techniques have been actively pursued in the last years, mainly due to the increasing number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight ensemble pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates and provide efficiency by reducing the ensemble size prior to combining the model. In this article, we present and validate an ensemble pruning approach for Optimum-Path Forest (OPF) classifiers based on metaheuristic optimization over general-purpose data sets to validate the effectiveness and efficiency of the proposed approach using distinct configurations in real and synthetic benchmark data sets, and thereafter, we apply the proposed approach in remote-sensing images to investigate the behaviour of the OPF classifier using pruning strategies. The image data sets were obtained from CBERS-2B, LANDSAT-5 TM, IKONOS-2 MS, and GEOEYE sensors, covering some areas of Brazil. The well-known Indian Pines data set was also used. In this work, we evaluate five different optimization algorithms for ensemble pruning, including that Particle Swarm Optimization, Harmony Search, Cuckoo Search, and Firefly Algorithm. In addition, we performed an empirical comparison between Support Vector Machine and OPF using the strategy of ensemble pruning. Experimental results showed the effectiveness and efficiency of ensemble pruning using OPF-based classification, especially concerning ensemble pruning using Harmony Search, which shows to be effective without degrading the performance when applied to large data sets, as well as a good data generalization.
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