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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Jaime Rodríguez-Fernández Tiago Pinto Francisco Silva Isabel Praça Zita Vale Juan Manuel Corchado GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Porto Portugal BISITE Research Centre University of Salamanca (USAL) Salamanca Spain

The electricity markets restructuring process encouraged the use of computational tools in order to allow the study of different market mechanisms and the relationships between the participating entities. Automated negotiation plays a crucial role in the decision support for energy transactions due to the constant need for players to engage in bilateral negotiations. This paper proposes a methodology to estimate bilateral contract prices, which is essential to support market players in their decisions, enabling adequate risk management of the negotiation process. The proposed approach uses an adaptation of the Q-Learning reinforcement learning algorithm to choose the best from a set of possible contract prices forecasts that are determined using several methods, such as artificial neural networks (ANN), support vector machines (SVM), among others. The learning process assesses the probability of success of each forecasting method, by comparing the expected negotiation price with the historic data contracts of competitor players. The negotiation scenario identified as the most probable scenario that the player will face during the negotiation process is the one that presents the higher expected utility value. This approach allows the supported player to be prepared for the negotiation scenario that is the most likely to represent a reliable approximation of the actual negotiation environment.

关键词： Contracts Electricity supply industry Tools Forecasting Adaptation models Learning (artificial intelligence) Estimation

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Hybrid Particle Swarm Optimization of Electricity Market Participation Portfolio

Hybrid Particle Swarm Optimization of Electricity Market Par...

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IEEE Symposium Series on Computational Intelligence

作者： Ricardo Faia Tiago Pinto Zita Vale Juan Manuel Corchado GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/ IPP) Porto Portugal BISITE Research Centre University of Salamanca (USAL) Salamanca Spain

This paper proposes a novel hybrid particle swarm optimization methodology to solve the problem of optimal participation in multiple electricity markets. The decision time is usually very important when planning the participation in electricity markets. This environment is characterized by the time available to take action, since different electricity markets have specific rules, which requires participants to be able to adapt and plan their decisions in a short time. Using metaheuristic optimization, participants' time problems can be resolved, because these methods enable problems to be solved in a short time and with good results. This paper proposes a hybrid resolution method, which is based on the particle swarm optimization metaheuristic. An exact mathematical method, which solves a simplified, linearized, version of the problem, is used to generate the initial solution for the metaheuristic approach, with the objective of improving the quality of results without representing a significant increase of the execution time.

关键词： Artificial intelligence Electricity markets Hybrid resolution methods Portfolio optimization Metaheuristic optimization

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Evolutionary framework for multi-dimensional signaling method applied to energy dispatch problems in smart grids 19

Evolutionary framework for multi-dimensional signaling metho...

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19th International Conference on intelligent System Application to Power Systems, ISAP 2017

作者： Lezama, Fernando De Cote, Enrique Munoz Sucar, Luis Enrique Soares, Joao Vale, Zita Mexico PROWLER.io Ltd Cambridge United Kingdom GECAD-Res. Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Portugal

ISBN: (纸本)9781509040001

In the smart grid (SG) era, the energy resource management (ERM) in power systems is facing an increase in complexity, mainly due to the high penetration of distributed resources, such as renewable energy and electric vehicles (EVs). Therefore, advanced control techniques and sophisticated planning tools are required to take advantage of the benefits that SG technologies can provide. In this paper, we introduce a new approach called multi-dimensional signaling evolutionary algorithm (MDS-EA) to solve the large-scale ERM problem in SGs. The proposed method uses the general framework from evolutionary algorithms (EAs), combined with a previously proposed rule-based mechanism called multi-dimensional signaling (MDS). In this way, the proposed MDS-EA evolves a population of solutions by modifying variables of interest identified during the evaluation process. Results show that the proposed method can reduce the complexity of metaheuristics implementation while achieving competitive solutions compared with EAs and deterministic approaches in acceptable times. © 2017 IEEE.

关键词： Evolutionary algorithms

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PersonalFit - Fitness App with intelligent plan generator 9

PersonalFit - Fitness App with intelligent plan generator

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9th International C* Conference on Computer Science and Software engineering, C3S2E 2016

作者： Oliveira, Tiago Leite, Diogo Marreiros, Goreti Computer Science: Information and Knowledge Systems Institute of Engineering-Polytechnic of Porto Porto Portugal GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering-Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781450340755

The growth of the mobile app market and the fitness market led to some interesting projects combining both areas to be developed over the last decade. In this paper we present PersonalFit an intelligent and adaptive prototype that helps gym clients to manage and adapt their training plans in a dynamic and personalized way, envisaging the achievement of better results. Three main modules compose PersonalFit: communications, Android Application and an intelligent plan generator that implements clustering techniques. Copyright is held by the owner/author(s).

关键词： Health

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Lighting consumption optimization using fish school search algorithm

Lighting consumption optimization using fish school search a...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Pedro Faria Ângelo Pinto Zita Vale Mahsa Khorram Fernando B. de Lima Neto Tiago Pinto GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development ISEP/IPP - Polytechnic of Porto Porto Portugal Polytechnic School of Engineering University of Pernambuco Recife Brazil BISITE Research Centre University of Salamanca (USAL) Salamanca Calle Espejo 12 Spain

Electricity consumption has increased all around the world in the last decades. This has caused a rise in the use of fossil fuels and in the harming of the environment. In the past years the use of renewable energies and reduction of consumption has growth in order to deal with that problem. The change in the production paradigm led to an increasing search of ways to shorten consumption and adapt to the production. One of the solutions for this problem is to use Demand Response systems. Lighting systems have a major role in electricity consumption, so they are very suitable to be applied in a Demand Response system, optimizing their use. This optimization can be made in different ways being one of them by using a heuristic algorithm. This paper focuses on the use of Fish School Search algorithm to optimize a lighting system, in order to understand its capability of dealing with a problem of this nature and compare it with other algorithms to evaluate its performance.

关键词： Fish Frequency selective surfaces Optimization Lighting Linear programming Production Load management

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A Residential Energy Management System with Offline Population-Based Optimization

A Residential Energy Management System with Offline Populati...

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IEEE Symposium Series on Computational Intelligence

作者： Joao Soares Fernando Lezama Sergio Ramos Zita Vale Andre Lopes GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto Porto Portugal

Expectable improvements in battery technology and lower prices will certainly contribute to increase the interest in residential energy storage systems in the near future. The installment of photovoltaic panels and the use of energy storage systems will help to reduce power losses in distribution and transmission power grid and increase network availability, and consequently, to reduce the dependency on the use of fossil fuels. The paper presents a light implementation of residential energy management system that integrates photovoltaic generation, an energy storage system and an electric vehicle. The goal of the system is to reduce the costs of electric consumer energy bill. The effectiveness of the system is verified through its application in several scenarios for the Portuguese context. An offline population-based algorithm, namely differential evolution method is used to adjust the objective function for the online control of the energy devices in the residential house.

关键词： Differential evolution Energy management system Energy costs Energy storage Electric vehicle Photovoltaic

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Model for the Integration of Distributed Energy Resources in Energy Markets by an Aggregator

Model for the Integration of Distributed Energy Resources in...

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IEEE Manchester PowerTech Conference

作者： Joao Spinola Pedro Faria Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering - Polytechnic of Porto (ISEP/IPP)

ISBN: (纸本)9781509042388

The increase of distributed energy resources in energy systems and current legislation concerning the participation in energy markets, is causing a high wasted potential of energy supply and flexibility services. In this paper, it is proposed a methodology for the management and integration of distributed energy resources in energy systems and markets, through the application of an aggregator. Also, the aggregator provides demand response programs based on tariffs, thus enabling different types of participations. Aggregation is performed using K-Means clustering algorithm, and serves as basis for remuneration, where the aggregated energy and cost of resources is obtained. Given this methodology, the aggregator obtains the energy available and the minimum sell cost to negotiate in market, with the intent of obtaining profit in its operation. The methodology is validated through a case study, with 20 consumers and 25 distributed generators.

关键词： Aggregator Clustering Demand response Distributed generation Energy markets

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Product Recommendation based on Shared Customer's Behaviour

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Procedia Computer Science 2016年 100卷 136-146页

作者： Fátima Rodrigues Bruno Ferreira GECAD Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Institute of Engineering – Polytechnic of Porto (ISEP/IPP)

Today consumers are exposed to an increasing variety of products and information never seen before. This leads to an increasing diversity of consumers’ demand, turning into a challenge for a retail store to provide the right products accordingly to customer preferences. Recommender systems are a tool to cope with this challenge, through product recommendation it is possible to fulfill customers’ needs and expectations, helping maintaining loyal customers while attracting new customers. However the huge size of transactional databases typical of retail business reduces the efficiency and quality of recommendations. In this paper a hybrid recommendation system that combines content-based, collaborative filtering and data mining techniques is proposed to surpass these difficulties. The recommendation algorithm starts to obtain similar groups of customers using customer lifetime value. Next an association rule mining approach based on similar shopping baskets of customers of the same cluster, in a specific time period is implemented in order to provide more assertive and personalized customer product recommendations. The algorithm was tested with data from a chain of perfumeries. The experimental results show that the proposed algorithm when compared with a base recommendation (made solely on past purchases of customers) can increase the value of the sales without losing recommendation accuracy.

关键词： clustering market basket association rules product recommendation

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House management system with real and virtual resources: Energy efficiency in residential microgrid

House management system with real and virtual resources: Ene...

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International Global Information Infrastructure Symposium (GIIS)

作者： Gabriel Santos Filipe Femandes Tiago Pinto Marco Silva Omid Abrishambaf Hugo Morais Zita Vale GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic of Porto Porto Portugal GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP - Polytechnic of Porto Porto Portugal

ISBN: (纸本)9781509036097

The reduction of the greenhouse gas emissions is a priority all around the globe. The investment on renewable energy sources is contributing for new opportunities in the context of the smart grids and microgrids. Recent advances are transforming the consumer into a prosumer, being able to adapt the consumption depending on its own generated power, and selling the surplus or buying the missing power. In this context, home management systems are emerging as an effective means to support the management of energy resources in the context of communication between functions/devices of a smart home. This paper presents a new agent-based home energy management approach, using ontologies to enable semantic communications between heterogeneous multi-agent entities. The main goal is to support an efficient energy management of end consumers in the context of microgrids, obtaining a scheduling for both real and virtual resources. A case study is presented, which simulates a 25-bus microgrid that includes a laboratorial controlled house (with real and simulated resources), which is managed by the proposed energy management system.

关键词： Ontologies Context Smart grids Microgrids Energy resources Energy efficiency

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Ekrucami architecture-applications in healthcare domain

Frontiers in Artificial Intelligence and Applications

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Frontiers in Artificial Intelligence and Applications 2017年 298卷 140-152页

作者： Ramos, Carlos Martinho, Diogo Marreiros, Goreti Villarrubia, Gabriel De Paz, Juan F. Corchado, Juan M. Ko, Hoon Kim, Haeng-Kon Yoe, Hyun GECAD-Research Group Intelligent Engineering and Computing for Advanced Innovation Development Institute of Engineering-Polytechnic Porto Portugal Departamento de Informática y Automática Universidad de Salamanca Plaza de la Merced s/n Salamanca37008 Spain Department of Computer Science and Engineering Sungkyunkwan University Korea Republic of Daegu Catholic University Korea Republic of Suncheon National University Korea Republic of

ISBN: (纸本)9781614998037

Ambient Intelligence is a paradigm in Artificial Intelligence which promotes intelligent computing and responds to the necessities of every person in many different environments whether it is a home, classroom, a shopping, or a hospital. Nowadays we are increasingly seeing and talking about issues such as aging populations or the increase in the number of patients with chronic diseases (among other issues). Looking at the solutions available in this area it is necessary to develop more effective systems that can support people and improve their quality of life. This work is a brief study on EKRUCAmI architecture and its suitability to the healthcare sector. Some healthcare systems will be presented which have been designed to use EKRUCAmI architecture. © 2017 The authors and IOS Press.

关键词： Ambient intelligence

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