ISEP - GECAD - Grupo de Investigação em Engenharia do Conhecimento a Apoio à Decisão
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GECAD is a research unit settled in the Institute of Engineering - Polytechnic of Porto (ISEP/IPP) having as mission the promotion and development of scientific research in the Knowledge and Decision Sciences domains, having Information Technologies as support. It involves 2 research groups: Intelligent Systems and Power Energy Systems. GECAD is known worldwide in its areas of research, leading some research domains. [-]
GECAD is coordinated by Prof. Zita Vale, and recognized by FCT (Portuguese Science and Technology Foundation). 79 researchers are involved in GECAD, including 37 with PhD degree. It is the largest R&D unit from the Polytechnic sub-system of Portugal.
GECAD was involved in more than 60 R&D projects (more than 20 on-going projects now) with external funding. We are one of the Portuguese R&D units with more success at this level. Just a number, 8, is the number of projects assigned to GECAD in the last FCT Call for Projects. GECAD has a tremendous success in publications in important scientific journals; many special issues of these journals are edited by GECAD researchers.
Understanding its responsibility for the Society development, GECAD has decided to adopt a new slogan: “Intelligence for a Sustainable, Safe, and Inclusive World”. For this reason, the most recent GECAD projects are applied to areas like Energy, Transportation, Environment, Economy, Inclusion, Critical Infrastructures, Security, Information Access and new ways of Socialization.
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- A multi-agent environment in roboticsPublication . Oliveira, Eugénio; Camacho, R.; Ramos, CarlosThe use of Multi-Agent Systems as a Distributed AI paradigm for Robotics is the principal aim of our present work. In this paper we consider the needed concepts and a suitable architecture for a set of Agents in order to make it possible for them to cooperate in solving non-trivial tasks. Agents are sets of different software modules, each one implementing a function required for cooperation. A Monitor, an Acquaintance and Self-knowledge Modules, an Agenda and an Input queue, on the top of each Intelligent System, are fundamental modules that guarantee the process of cooperation, while the overall aim is devoted to the community of cooperative Agents. These Agents, which our testbed concerns, include Vision, Planner, World Model and the Robot itself.
- Addressing the facilities layout design problem through constraint logic programmingPublication . Tavares, José; Ramos, Carlos; Neves, JoséOne of the most difficult problems that face researchers experimenting with complex systems in real world applications is the Facility Layout Design Problem. It relies with the design and location of production lines, machinery and equipment, inventory storage and shipping facilities. In this work it is intended to address this problem through the use of Constraint Logic Programming (CLP) technology. The use of Genetic Algorithms (GA) as optimisation technique in CLP environment is also an issue addressed. The approach aims the implementation of genetic algorithm operators following the CLP paradigm.
- Decision-Support Tool for the Establishment of Contracts in the Electricity MarketPublication . Azevedo, Filipe; Vale, Zita; Vale, António A.The Pool, in many countries, was adopted for the participants of the electricity market to trade the electrical energy in a basis of each half-hour or one hour of the next day. However, like the traditional markets, the agents of electrical market are now exposed to the volatility of market price. In some countries, to face that problem and to turn the market more liquid, the derivatives markets – futures and options - were introduced to negotiate products with electrical energy as underlying active. In this context, there is a need of decisionsupport tools to assist those agents for the use of derivatives markets with the objective of practicing the hedge. In this paper, we present a decision model that supports producers to establish contracts with the objective to maximize the profit expected utility.
- Hedging Using Futures and Options Contracts in the Electricty MarketPublication . Azevedo, Filipe; Vale, Zita; Vale, António A.Since the 80’s with the experience of Chile, the electric sector has suffered, in many counties, a process of deregulation and liberalization. In almost of the countries, that process originated the appearance of a Pool where the participants of the market trade the electrical energy on a basis of half-hour or one hour of the next day. However, like the traditional markets, the agents of electricity markets are now exposed to the volatility of market price, so far inexistent in those markets. In some countries, to face that problem and to turn the market more liquid have been introduced derivatives markets – futures and options, to negotiate products with underlying active the electrical energy. In this context, there is a need of decision-support tools that allow those agents to use derivatives markets with the objective of practicing the hedge and simultaneously increase their results. In this paper, we present a decision model that supports producers in the establishment of contracts with the objective to maximize the profit expected utility. The paper presents a group of examples of the use of this decision-support system.
- MASCEM: A Multiagent System That Simulates Competitive Electricity MarketsPublication . Praça, Isabel; Ramos, Carlos; Vale, Zita; Cordeiro, ManuelAround the world, the electricity industry, which has long been dominated by vertically integrated utilities, is experiencing major changes in the structure of its markets and regulations. Owing to new regulations, it's evolving into a distributed industry in which market forces drive electricity's price. The industry is becoming competitive; a market environment is replacing the traditional centralized-operation approach. This transformation is often called the deregulation of the electricity market. MASCEM, a multiagent simulator system, is a valuable framework for evaluating new rules, new behavior, and new participants in the numerous electricity markets that are moving toward liberalization and competition.
- Optimal Contracts Allocation Using Mean Variance Optimization MethodPublication . Azevedo, Filipe; Vale, ZitaThe process of restructuration and liberalization of power systems are a constant all over the world. However, those processes, due to the specific characteristics of the “product” electricity, create uncertainty and new risks that did not exist when power systems were vertically integrated. Those changes origin the necessity of tools that allow the participants of the electricity markets to practice the hedge against the volatility of the System Marginal Price. In that sense, we present in this paper a decision-support application, based on a Mean Variance Optimization Method trying to give a response to the necessities of the electricity markets participants. The results show that the proposed method can be useful to producers and also to others participants of electricity markets like Brokers and Load Serving Entities (LSE).
- Short-term Price Forecast From Risk Management Point of ViewPublication . Azevedo, Filipe; Vale, ZitaThis paper provides a different approach for electricity price forecast from risk management point of view. Making use of neural networks, the methodology presented here has as main concern finding the maximum and the minimum System Marginal Price (SMP) for a specific programming period, with a certain confidence level. To train the neural network, probabilistic information from past years is used. This approach was developed with the objective of integrating a decisionsupport system that uses Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data are presented and discussed in detail.
- Production Support-Support System on Liberalized Market EnvironmentPublication . Azevedo, Filipe; Vale, ZitaThe restructuration and liberalization processes of power systems are a constant all over the world. However, those processes due to the specific characteristics of the product electricity create uncertainty and new risks that doesn t exist when power systems were vertically integrated. Those changes, origin the necessity of tools that allow the participants of the electricity markets to practice the hedge against the volatility of the System Marginal Price. In that sense, we present in this paper a Mean Variance Optimization Method trying to give a response to the necessities of the electricity markets participants. This optimization method was applied on an example presented in this paper. We conclude that, the Optimization Method presented in this paper, could be useful to producers and also to others participants.
- An Electric Energy Consumer Characterization Framework Based on Data Mining TechniquesPublication . Figueiredo, Vera; Rodrigues, Fátima; Vale, Zita; Gouveia, Joaquim BorgesThis paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company.
- Optimal Short-term Contract Allocation Using Particle Swarm OptimizationPublication . Azevedo, Filipe; Vale, ZitaIn a liberalized electricity market, participants have several types of contracts to sell or buy electrical energy. Increasing electricity markets liquidity and, simultaneously, providing to market participants tools for hedging against spot electricity price were the two main reasons for the appearance of those types of contracts. However, due to the payoff nonlinearity characteristic of those contracts, deciding the optimal portfolio that best adjusts to their necessities becomes a hard task. This paper presents an optimization model applied to optimal contract allocation using Particle Swarm Optimization (PSO). This optimization model consists on finding the portfolio that maximizes the electricity producer results and simultaneously allows the practice of the hedge against the volatility of the System Marginal Price (SMP). Risk management is considered through the consideration of a mean-variance optimization function. An example for a programming period is presented using spot, forward and options contracts. PSO performance in such type of problems is evaluated by comparing it with the Genetic Algorithms (GA).