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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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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 challeng. for a retail store to provide the right products accordingly to customer preferences. Recommender systems are a tool to cope with this challeng., 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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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 eng.neering, 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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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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Energy Consumption Forecasting using Neuro-Fuzzy Inference Systems: Thales TRT building case study

Energy Consumption Forecasting using Neuro-Fuzzy Inference S...

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IEEE Symposium Series on Computational Intelligence

作者： Aria Jozi Tiago Pinto Isabel Praca Sergio Ramos Zita Vale Benedicte Goujon Teodora Petrisor GECAD - Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/ IPP) Porto Portugal Thales Research & Technology France Palaiseau France

Electrical energy consumption forecasting is, nowadays, essential in order to deal with the new paradigm of consumers' active participation in the power and energy system. The uncertainty related to the variability of consumption is associated to numerous factors, such as consumers' habits, the environmental temperature, luminosity, etc. Current forecasting methods are not suitable to deal with such a combination of input variables, with often highly variable influence on the outcomes of the actual energy consumption. This paper presents a study on the application of five different methods based on fuzzy rule-based systems. This type of method is able to find associations between the distinct input variables, thus creating rules that support and improve the actual forecasting process. A case study is presented, showing the results of applying these five methods to predict the consumption of a real building: the Thales TRT building, in France.

关键词： Energy consumption Forecasting Fuzzy rule-based systems TRT building

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Wang and Mendel's fuzzy rule learning method for energy consumption forecasting considering the influence of environmental temperature

Wang and Mendel's fuzzy rule learning method for energy cons...

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International Global Information Infrastructure Symposium (GIIS)

作者： Aria Jozi Tiago Pinto Isabel Praça Francisco Silva Brígida Teixeira Zita Vale GECAD-Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic of Porto Porto Portugal

Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

关键词： Energy consumption Buildings Forecasting Databases Learning systems Weather forecasting

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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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Optimization of electricity markets participation with QPSO

Optimization of electricity markets participation with QPSO

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International Conference on European Electricity Market, EEM

作者： Ricardo Faia Tiago Pinto Zita Vale GECAD-Research Group on Intelligent Engineering Computing for Advanced Innovation and Development Polytechnic of Porto (ISEP/IPP) Porto Portugal

All around the world, the electric sector has suffered significant changes. With these alterations, electrical systems have become international, with several countries connected by a system where the management is done in common grounds. With the incorporation of large scale distributed generation, competitiveness in electrical markets has increased as small generators unite in order to be able to compete with large producers. In this game where the main objective is to win, and the premium is money it is necessary to be keen to be able to sell the available electricity at the best possible prices. With the objective of supporting players' decisions, decision support tools play a crucial role. These tools enable market players with suggestions of actions to increase their advantage from market participation. This paper presents a Quantum-based Particle Swarm Optimization (QPSO) methodology to solve the problem of optimal participation in multiple electricity markets.

关键词： Optimization Electricity supply industry Portfolios Particle swarm optimization Biological system modeling Quantum mechanics Contracts

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Allocation of fixed costs considering Distributed Generation and distinct approaches of Demand Response remuneration in distribution networks

Allocation of fixed costs considering Distributed Generation...

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Clemson University Power Systems Conference (PSC)

作者： Fábio Pereira João Soares Pedro Faria Zita Vale GECAD — Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development IPP-Polytechnic Institute of Porto Porto Portugal

In the present context the distribution networks are characterized by the increasing penetration of Distributed Generation (DG) resources, which imposes important changes in operation practice. Moreover, the concept of Demand Response (DR) is currently a very important resource. The present paper proposes a methodology that makes possible the allocation of fixed costs in distribution networks considering the integration of DG and DR resources. It considers several possibilities for consumers DR remuneration. The fixed cost allocation is determined in three phases - DC Optimal Power Flow (DC-OPF), Kirschen's tracing method, and MW-mile method. The presented case study contemplates a distribution network with 49 buses, 47 DG units, 4 external suppliers and 50 consumers.

关键词： Remuneration Resource management Density estimation robust algorithm Load flow Indexes Distributed power generation Optimal scheduling

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