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.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Balancing market integration in MASCEM electricity market simulator

With the restructuring of the energy sector in industrialized countries there is an increased complexity in market players’ interactions along with emerging problems and new issues to be addressed. Decision support tools that facilitate the study and understanding of these markets are extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent simulator for competitive electricity markets. It is essential to reinforce MASCEM with the ability to recreate electricity markets reality in the fullest possible extent, making it able to simulate as many types of markets models and players as possible. This paper presents the development of the Balancing Market in MASCEM. A key module to the study of competitive electricity markets, as it has well defined and distinct characteristics previously implemented.

Complex market integration in MASCEM electricity market simulator

The restructuring that the energy sector has suffered in industrialized countries originated a greater complexity in market players’ interactions, and thus new problems and issues to be addressed. Decision support tools that facilitate the study and understanding of these markets become extremely useful to provide players with competitive advantage. In this context arises MASCEM, a multi-agent system for simulating competitive electricity markets. To provide MASCEM with the capacity to recreate the electricity markets reality in the fullest possible extent, it is essential to make it able to simulate as many market models and player types as possible. This paper presents the development of the Complex Market in MASCEM. This module is fundamental to study competitive electricity markets, as it exhibits different characteristics from the already implemented market types.

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units.

Multi-apprentice learning for meta-heuristics parameter tuning in a multi agent scheduling system

The scheduling problem is considered in complexity theory as a NP-hard combinatorial optimization problem. Meta-heuristics proved to be very useful in the resolution of this class of problems. However, these techniques require parameter tuning which is a very hard task to perform. A Case-based Reasoning module is proposed in order to solve the parameter tuning problem in a Multi-Agent Scheduling System. A computational study is performed in order to evaluate the proposed CBR module performance.

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.

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.

A study on the encryption algorithm for RFID tag (SEED : 8 Rounds × 64 bits block)

Although it is always weak between RFID Tag and Terminal in focus of the security, there are no security skills in RFID Tag. Recently there are a lot of studying in order to protect it, but because it has some physical limitation of RFID, that is it should be low electric power and high speed, it is impossible to protect with the skills. At present, the methods of RFID security are using a security server, a security policy and security. One of them the most famous skill is the security module, then they has an authentication skill and an encryption skill. In this paper, we designed and implemented after modification original SEED into 8 Round and 64 bits for Tag.

FPGA-based weblab infrastructures guidelines and a prototype implementation example

Recent trends show an increasing number of weblabs, implemented at universities and schools, supporting practical training in technical courses and providing the ability to remotely conduct experiments. However, their implementation is typically based on individual architectures, unable of being reconfigured with different instruments/modules usually required by every experiment. In this paper, we discuss practical guidelines for implementing reconfigurable weblabs that support both local and remote control interfaces. The underlying infrastructure is based on reconfigurable, low-cost, FPGA-based boards supporting several peripherals that are used for the local interface. The remote interface is powered by a module capable of communicating with an Ethernet based network and that can either correspond to an internal core of the FPGA or an external device. These two approaches are discussed in the paper, followed by a practical implementation example.

On-line self-healing of circuits implemented on reconfigurable FPGAs

To boost logic density and reduce per unit power consumption SRAM-based FPGAs manufacturers adopted nanometric technologies. However, this technology is highly vulnerable to radiation-induced faults, which affect values stored in memory cells, and to manufacturing imperfections. Fault tolerant implementations, based on Triple Modular Redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like module placement, the effects of multi- bit upsets (MBU) or fault accumulation, have also to be addressed. In case of a fault occurrence the correct operation of the affected module must be restored and/or the current state of the circuit coherently re-established. A solution that enables the autonomous restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in real-time, while keeping the normal operation of the circuit, is presented in this paper.

A framework for self-healing radiation-tolerant implementations on reconfigurable FPGAs

To increase the amount of logic available in SRAM-based FPGAs manufacturers are using nanometric technologies to boost logic density and reduce prices. However, nanometric scales are highly vulnerable to radiation-induced faults that affect values stored in memory cells. Since the functional definition of FPGAs relies on memory cells, they become highly prone to this type of faults. Fault tolerant implementations, based on triple modular redundancy (TMR) infrastructures, help to keep the correct operation of the circuit. However, TMR is not sufficient to guarantee the safe operation of a circuit. Other issues like the effects of multi-bit upsets (MBU) or fault accumulation, have also to be addressed. Furthermore, in case of a fault occurrence the correct operation of the affected module must be restored and the current state of the circuit coherently re-established. A solution that enables the autonomous correct restoration of the functional definition of the affected module, avoiding fault accumulation, re-establishing the correct circuit state in realtime, while keeping the normal operation of the circuit, is presented in this paper.

Habitação modular evolutiva

Da necessidade de criação de habitações que se adaptem ao constante dinamismo do ser humano, surgiu a ideia da concepção de um edifício evolutivo com base na construção modular. A construção modular poderá ser entendida como sendo a principal resposta ao combate da construção de estruturas físicas e imóveis, com características inalteráveis ao longo do tempo. Com este trabalho idealizou-se uma habitação evolutiva, baseado nos conceitos da Coordenação Modular, identificando-se as suas especificações, exigências de desempenho e características funcionais. Foi considerada como medida modular base 0,10m. Os módulos foram pensados com o intuito de que o seu desempenho prático seja satisfatório. A sua base estrutural foi executada a partir do rearranjo de contentores marítimos. Previu-se ainda, o uso de coberturas verdes por serem uma solução comprovada de minorar notoriamente o efeito pernicioso que a construção tem sobre o meio ambiente, ao mesmo tempo que apresentam vantagens térmicas e acústicas. A questão do incentivo do uso da pré-fabricação em Portugal a uma maior escala, não poderia ser descurada. A apresentação de eventuais desenvolvimentos futuros é também relevante, dada a importância que é inerente ao âmbito da industrialização da construção. Do estudo realizado ficaram ainda, bem patentes, os benefícios da racionalização e da industrialização, tanto em termos económicos como em termos ambientais. É possível executar um projecto com níveis de perdas mais baixos e com índices de qualidade superiores a um menor preço.

Fonte de Iluminação LED – Avaliação dos Efeitos Fotobiológicos no Olho e Pele

Tendo por referência a diretiva 2006/95/CE, o trabalho desenvolvido no contexto da disciplina de Dissertação/Projeto/Estágio do Mestrado de Engenharia de Instrumentação e Metrologia, decorreu nas instalações do IEP (Instituto Electrotécnico Português) e teve como objetivo principal o desenvolvimento de um procedimento de avaliação dos efeitos fotobiológicos no olho e pele provocados por fontes de emissão contínua (LED), doravante designado método alternativo ao de referência. Os dois métodos, alternativo e de referência, utilizam respectivamente um foto-radiómetro multicanal e um espetro-radiómetro. O procedimento desenvolvido (método alternativo) de acordo com a norma EN/IEC62471) consiste na aquisição dos valores de irradiância com recurso a um foto-radiómetro e posterior determinação dos valores da radiância, com os quais se faz a avaliação dos efeitos fotobiológicos, para fontes de luz LED (Light Emitting Diode) ou GLS (General Lighting Service). A consulta detalhada da norma EN/IEC62471 e a pesquisa sobre os conceitos, definições, equipamentos e metodologias relacionadas com o tema em causa, constituiu o primeiro passo deste projecto. Com recurso aos dois equipamentos, uma fonte de luz LED (módulo de 12 lâmpadas LED) é avaliada em relação aos perigos (ou riscos) actínico UV e UV-A, ao perigo da luz azul e ainda o perigo térmico na retina e térmico na pele, permitindo fazer uma análise comparativa dos resultados. O método alternativo revelou-se bastante flexível e eficaz, proporcionando bons resultados em termos da irradiância e radiância dos referidos efeitos fotobiológicos. A comparação destes resultados com os valores limites de exposição mencionados na norma EN/IEC6247 permitiu afirmar que a fonte de luz LED avaliada não representa perigo fotobiológico para a saúde humana e classifica-se no grupo de risco “isento”. Uma vez cumpridos os objectivos, entendeu-se que seria uma mais-valia para o trabalho já realizado, estudar outro caso prático. Sendo assim, fez-se a avaliação da radiação de apenas um dos LED´s que constituíam a fonte usada nos ensaios anteriores, com o espetro-radiómetro (método de referência) e com uma distância de 200 mm entre a fonte e o medidor. Neste caso verificaram-se diferenças significativas nas quantidades obtidas quando comparadas com os valores normativos. Concluiu-se que o efeito fotobiológico da luz azul insere-se no grupo de “isento”, sem perigo para a saúde. Contudo, o efeito térmico da retina apresenta um aumento considerável da quantidade de radiância, embora dentro do grupo de risco “isento”. Esta classificação de grupos de risco. Face aos resultados obtidos, pode confirmar-se que as lâmpadas LED apresentam segurança fotobiológica, atendendo aos baixos valores de irradiância e radiância dos efeitos fotobiológicos estudados. Pode ainda afirmar-se que a utilização do foto-radiómetro em alternativa ao espetro-radiómetro se revela mais eficaz do ponto de vista de metodologia prática. Este trabalho demonstra a robustez desses dois equipamentos de avaliação dos efeitos fotobiológicos, e procura estabelecer uma linha de orientação para a prevenção dos efeitos adversos na pele e olhos de todos os seres humanos sujeitos à radiação ótica artificial. Quanto às incertezas de medições, em relação ao processo de medição com foto-radiómetro, a sua estimação não se realizou, devido a não rastreabilidade entre as medições indicadas pelo fabricante, no certificado de calibração e as medidas realizadas por outras entidades. Contudo, é propõe-se a sua realização em trabalhos futuros dentro desse âmbito. As incertezas dos resultados de medições com espetro-radiómetro foram parcialmente estimadas. Atendendo às potencialidades do sistema de medição, propõe-se como trabalho futuro, a aplicação da norma IEC62478, que faz parte da aplicação da norma EN/IEC62471 na avaliação do efeito da luz azul, com base na determinação da temperatura de cor correlacionada (CCT) de lâmpadas ou sistemas de lâmpadas incluindo luminárias. Os valores de irradiância e radiância adquiridos nos processos de avaliação, tanto com foto-radiómetro como espectro-radiómetro foram gravados em ficheiro Excel para um CD e anexados a este trabalho.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.