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.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

The verification of temporal KBS: SPARSE - a case study in power systems

In this paper we present VERITAS, a tool that focus time maintenance, that is one of the most important processes in the engineering of the time during the development of KBS. The verification and validation (V&V) process is part of a wider process denominated knowledge maintenance, in which an enterprise systematically gathers, organizes, shares, and analyzes knowledge to accomplish its goals and mission. The V&V process states if the software requirements specifications have been correctly and completely fulfilled. The methodologies proposed in software engineering have showed to be inadequate for Knowledge Based Systems (KBS) validation and verification, since KBS present some particular characteristics. VERITAS is an automatic tool developed for KBS verification which is able to detect a large number of knowledge anomalies. It addresses many relevant aspects considered in real applications, like the usage of rule triggering selection mechanisms and temporal reasoning.

LMP triggered real time demand response events

Recent changes in power systems mainly due to the substantial increase of distributed generation and to the operation in competitive environments has created new challenges to operation and planning. In this context, Virtual Power Players (VPP) can aggregate a diversity of players, namely generators and consumers, and a diversity of energy resources, including electricity generation based on several technologies, storage and demand response. Demand response market implementation has been done in recent years. Several implementation models have been considered. An important characteristic of a demand response program is the trigger criterion. A program for which the event trigger depends on the Locational Marginal Price (LMP) used by the New England Independent System operator (ISO-NE) inspired the present paper. This paper proposes a methodology to support VPP demand response programs management. The proposed method has been computationally implemented and its application is illustrated using a 32 bus network with intensive use of distributed generation. Results concerning the evaluation of the impact of using demand response events are also presented.

Simulated annealing metaheuristic to solve the optimal power flow

The optimal power flow problem has been widely studied in order to improve power systems operation and planning. For real power systems, the problem is formulated as a non-linear and as a large combinatorial problem. The first approaches used to solve this problem were based on mathematical methods which required huge computational efforts. Lately, artificial intelligence techniques, such as metaheuristics based on biological processes, were adopted. Metaheuristics require lower computational resources, which is a clear advantage for addressing the problem in large power systems. This paper proposes a methodology to solve optimal power flow on economic dispatch context using a Simulated Annealing algorithm inspired on the cooling temperature process seen in metallurgy. The main contribution of the proposed method is the specific neighborhood generation according to the optimal power flow problem characteristics. The proposed methodology has been tested with IEEE 6 bus and 30 bus networks. The obtained results are compared with other wellknown methodologies presented in the literature, showing the effectiveness of the proposed method.

Ant colony search algorithm for the optimal power flow problem

To maintain a power system within operation limits, a level ahead planning it is necessary to apply competitive techniques to solve the optimal power flow (OPF). OPF is a non-linear and a large combinatorial problem. The Ant Colony Search (ACS) optimization algorithm is inspired by the organized natural movement of real ants and has been successfully applied to different large combinatorial optimization problems. This paper presents an implementation of Ant Colony optimization to solve the OPF in an economic dispatch context. The proposed methodology has been developed to be used for maintenance and repairing planning with 48 to 24 hours antecipation. The main advantage of this method is its low execution time that allows the use of OPF when a large set of scenarios has to be analyzed. The paper includes a case study using the IEEE 30 bus network. The results are compared with other well-known methodologies presented in the literature.

Fontes alimentares de Triatoma pseudomaculata no Estado do Ceará, Brasil

OBJETIVO: Triatoma pseudomaculata, espécie peridomiciliar, é encontrada apresentando baixa taxa de infecção por Trypanosoma cruzi. Com o objetivo de identificar os possíveis reservatórios de T. cruzi, investigou-se a ocorrência desse triatomíneo no domicílio, bem como suas fontes alimentares. MÉTODOS: De janeiro de 2001 a julho de 2002 foram capturados 921 espécimes de T. pseudomaculata em 13 municípios do sul do Estado do Ceará. O conteúdo intestinal dos triatomíneos foi retirado, espalhado em disco de papel de filtro e analisado por precipitina para os seguintes anti-soros: ave, roedor, cão, gambá, lagarto, boi/cabra, gato, porco, barata e humano. A investigação da presença de T. cruzi foi feita observando-se parte do conteúdo intestinal dos insetos a fresco, entre lâmina e lamínula, e pela sua semeadura em meio de cultura. RESULTADOS: Do total examinado, 184 (90,6%) foram positivos para os anti-soros testados: ave (62,5%)> roedor (33,7%)> cão (20,1%)> gambá (9,8%)> lagarto (5%)> boi-cabra (5%)> gato (2,7%)> porco (2,2%)> barata (2,2%)> humano (1,6%). As alimentações variaram de zero (não reagiram) a quatro da seguinte forma: não reagiram (9,4%), uma (57,1%), duas (26%), três (7%) ou quatro (0,5%). Das fontes alimentares identificadas apenas três espécimes (1,6%) foram positivos para T. cruzi. CONCLUSÕES: A baixa incidência de sangue humano mostra que T. pseudomaculata está bem adaptado ao peridomicílio. Porém, a vigilância epidemiológica na região sul do Estado do Ceará se faz necessária tendo em vista a proximidade da espécie ao domicílio.

Negotiation mechanism for self-organized scheduling system

This paper presents a negotiation mechanism for Dynamic Scheduling based on Swarm Intelligence (SI). Under the new negotiation mechanism, agents must compete to obtain a global schedule. SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviors of insects and other animals. This work is concerned with negotiation, the process through which multiple selfinterested agents can reach agreement over the exchange of operations on competitive resources.

Auto-parametrização de meta-heurísticas para escalonamento dinâmico

Este artigo aborda o problema da parametrização de Técnicas de Optimização Inspiradas na Biologia (BIT - Biological Inspired Optimization Techniques), também conhecidas como Meta-heurísticas, considerando a importância que estas técnicas têm na resolução de situações de mundo real, sujeitas a perturbações externas. É proposto um módulo de aprendizagem com o objectivo de permitir que um Sistema Multi-Agente (SMA) para Escalonamento seleccione automaticamente uma Metaheurística e escolha a parametrização a usar no processo de optimização. Para o módulo de aprendizagem foi usado o Raciocínio baseado em Casos (RBC), permitindo ao sistema aprender a partir da experiência acumulada na resolução de problemas similares. Através da análise dos resultados obtidos é possível concluir acerca das vantagens da sua utilização.

Case-based reasoning for self-optimizing behavior

In this paper we present a Self-Optimizing module, inspired on Autonomic Computing, acquiring a scheduling system with the ability to automatically select a Meta-heuristic to use in the optimization process, so as its parameterization. Case-based Reasoning was used so the system may be able of learning from the acquired experience, in the resolution of similar problems. From the obtained results we conclude about the benefit of its use.

Collective intelligence on dynamic manufacturing scheduling optimization

Swarm Intelligence (SI) is a growing research field of Artificial Intelligence (AI). SI is the general term for several computational techniques which use ideas and get inspiration from the social behaviours of insects and of other animals. This paper presents hybridization and combination of different AI approaches, like Bio-Inspired Techniques (BIT), Multi-Agent systems (MAS) and Machine Learning Techniques (ML T). The resulting system is applied to the problem of jobs scheduling to machines on dynamic manufacturing environments.

Meta-heuristics tunning using CBR for dynamic scheduling

This paper addresses the problem of Biological Inspired Optimization Techniques (BIT) parameterization, considering the importance of this issue in the design of BIT especially when considering real world situations, subject to external perturbations. A learning module with the objective to permit a Multi-Agent Scheduling System to automatically select a Meta-heuristic and its parameterization to use in the optimization process is proposed. For the learning process, Casebased Reasoning was used, allowing the system to learn from experience, in the resolution of similar problems. Analyzing the obtained results we conclude about the advantages of its use.

Self-optimizing through CBR learning

In this paper, we foresee the use of Multi-Agent Systems for supporting dynamic and distributed scheduling in Manufacturing Systems. We also envisage the use of Autonomic properties in order to reduce the complexity of managing systems and human interference. By combining Multi-Agent Systems, Autonomic Computing, and Nature Inspired Techniques we propose an approach for the resolution of dynamic scheduling problem, with Case-based Reasoning Learning capabilities. The objective is to permit a system to be able to automatically adopt/select a Meta-heuristic and respective parameterization considering scheduling characteristics. From the comparison of the obtained results with previous results, we conclude about the benefits of its use.

Autonomic computing for scheduling in manufacturing systems

We describe a novel approach to scheduling resolution by combining Autonomic Computing (AC), Multi-Agent Systems (MAS) and Nature Inspired Optimization Techniques (NIT). Autonomic Computing has emerged as paradigm aiming at embedding applications with a management structure similar to a central nervous system. A natural Autonomic Computing evolution in relation to Current Computing is to provide systems with Self-Managing ability with a minimum human interference. In this paper we envisage the use of Multi-Agent Systems paradigm for supporting dynamic and distributed scheduling in Manufacturing Systems with Autonomic properties, in order to reduce the complexity of managing systems and human interference. Additionally, we consider the resolution of realistic problems. The scheduling of a Cutting and Treatment Stainless Steel Sheet Line will be evaluated. Results show that proposed approach has advantages when compared with other scheduling systems.