Browsing by Author "Corchado, Juan Manuel"
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- An Intelligent Coaching Prototype for Elderly CarePublication . Martinho, Diogo; Crista, Vítor; Carneiro, João; Corchado, Juan Manuel; Marreiros, GoretiThe world ageing problem is prompting new sustainable ways to support elderly people. As such, it is important to promote personalized and intelligent ways to assure the active and healthy ageing of the population. Technological breakthroughs have led to the development of personalized healthcare systems, capable of monitoring and providing feedback on different aspects that can improve the health of the elderly person. Furthermore, defining motivational strategies to persuade the elderly person to be healthier and stay connected to such systems is also fundamental. In this work, a coaching system is presented, especially designed to support elderly people and motivate them to pursue healthier ways of living. To do this, a coaching application is developed using both a cognitive virtual assistant to directly interact with the elderly person and provide feedback on his/her current health condition, and several gamification techniques to motivate the elderly person to stay engaged with the application. Additionally, a set of simulations were performed to validate the proposed system in terms of the support and feedback provided to the user according to his progress, and through interactions with the cognitive assistant.
- An Optimization Model for Energy Community Costs Minimization Considering a Local Electricity Market between Prosumers and Electric VehiclesPublication . Faia, Ricardo; Soares, João; Vale, Zita; Corchado, Juan ManuelElectric vehicles have emerged as one of the most promising technologies, and their mass introduction may pose threats to the electricity grid. Several solutions have been proposed in an attempt to overcome this challenge in order to ease the integration of electric vehicles. A promising concept that can contribute to the proliferation of electric vehicles is the local electricity market. In this way, consumers and prosumers may transact electricity between peers at the local community level, reducing congestion, energy costs and the necessity of intermediary players such as retailers. Thus, this paper proposes an optimization model that simulates an electric energy market between prosumers and electric vehicles. An energy community with different types of prosumers is considered (household, commercial and industrial), and each of them is equipped with a photovoltaic panel and a battery system. This market is considered local because it takes place within a distribution grid and a local energy community. A mixed-integer linear programming model is proposed to solve the local energy transaction problem. The results suggest that our approach can provide a reduction between 1.6% to 3.5% in community energy costs
- Automated combination of bilateral energy contracts negotiation tacticsPublication . Pinto, Angelo; Pinto, Tiago; Silva, Francisco; Praça, Isabel; Vale, Zita; Corchado, Juan ManuelThis paper addresses the theme automated bilateral negotiation of energy contracts. In this work, the automatic combination between different negotiation tactics is proposed. This combination is done dynamically throughout the negotiation process, as result from the online assessment that is performed after each proposal and counter-proposal. The proposed method is integrated in a decision support system for bilateral negotiations, called Decision Support for Energy Contracts Negotiations (DECON), which in turn is integrated with the Multi-Agent Simulator of Competitive Electricity Markets (MASCEM). This integration enables testing and validating the proposed methodology in a realistic market negotiation environment. A case study is presented, demonstrating the advantages of the proposed approach.
- Bilateral contract prices estimation using a Q-learning based approachPublication . Rodriguez-Fernandez, Jaime; Pinto, Tiago; Silva, Francisco; Praça, Isabel; Vale, Zita; Corchado, Juan ManuelThe 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.
- Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy managementPublication . Faia, R.; Pinto, Tiago; Abrishambaf, Omid; Fernandes, Filipe; Vale, Zita; Corchado, Juan ManuelThis paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.
- Case-based reasoning using expert systems to determine electricity reduction in residential buildingsPublication . Faia, Ricardo; Pinto, Tiago; Vale, Zita; Corchado, Juan ManuelCase-based reasoning enables solving new problems using past experience, by reusing solutions for past problems. The simplicity of this technique has made it very popular in several domains. However, the use of this type of approach to support decisions in the power and energy domain is still rather unexplored, especially regarding the flexibility of consumption in buildings in response to recent environmental concerns and consequent governmental policies that envisage the increase of energy efficiency. In order to determine the amount of consumption reduction that should be applied in a building, this article proposes a methodology that adapts the past results of similar cases in order to achieve a decision for the new case. A clustering methodology is used to identify the most similar previous cases, and an expert system is developed to refine the final solution after the combination of the similar cases results. The proposed CBR methodology is evaluated using a set of real data from a residential building. Results prove the advantages of the proposed methodology, demonstrating its applicability to enhance house energy management systems by determining the amount of reduction that should be applied in each moment, thus allowing such systems to carry out the reduction through the different loads of the building.
- D7.3 Proceedings of the Second DREAM-GO Workshop: Real-Time Demand Response and Intelligent Direct Load ControlPublication . Vale, Zita; Khorram Ghahfarrokhi, Mahsa; Faria, Pedro; Spínola, João; Canizes, Bruno; Pinto, Tiago; Soares, João; Chamoso, Pablo; Santos, Daniel; Garcia, Oscar; Catalina, Jorge; Guevarra, Fabio; Navarro-Cáceres, María; Gazafroudi, Amin Shokri; Prieto-Castrillo, Francisco; Corchado, Juan Manuel; Santos, Gabriel; Teixeira, Brígida; Praça, Isabel; Sousa, Filipe; Zawislak, Krzysztof; Iglesia, Daniel Hernández de la; Barriuso, Alberto Lopez; Lozano, Alvaro; Herrero, Jorge Revuelta; Landeck, Jorge; Paz, Juan F. de; Corchado, Juan M.; Garcia, Ruben Martin; González, Gabriel Villarrubia; Bajo, Javier; Matos, Luisa; Klein, L. Pires; Carreira, R.; Torres, I.; Landeck, JorgeProceedings of the Second DREAM-GO Workshop Real-Time Demand Response and Intelligent Direct Load Control
- D7.4 - Proceedings of the Third DREAM-GO Workshop: Intelligent load management in local and wholesale demand response marketsPublication . Barriuso, Alberto L.; Briones, Alfonso González; Lozano, Álvaro; Gazafroudi, Amin Shokri; Iglesia, Daniel H. de la; Sousa, Filipe; Villarrubia, Gabriel; Spínola, João; Revuelta Herrero, Jorge; Paz, Juan F. de; Corchado, Juan Manuel; Venyagamoorthy, Kumar G.; Gomes, Luis; Khorram Ghahfarrokhi, Mahsa; Navarro-Cáceres, María; Abrishambaf, Omid; Faria, Pedro; Castro, Rafael; Silva, Sergio; Coppens, Tom; Vale, ZitaProceedings of the Third DREAM-GO Workshop: Intelligent load management in local and wholesale demand response markets - Deliverable 7.4
- Differential Evolution Aplication in Portfolio optimization for Electricity MarketsPublication . Faia, R.; Lezama, Fernando; Soares, João; Vale, Zita; Pinto, Tiago; Corchado, Juan ManuelSmart Grid technologies enable the intelligent integration and management of distributed energy resources. Also, the advanced communication and control capabilities in smart grids facilitate the active participation of aggregators at different levels in the available electricity markets. The portfolio optimization problem consists in finding the optimal bid allocation in the different available markets. In this scenario, the aggregator should be able to provide a solution within a timeframe. Therefore, the application of metaheuristic approaches is justified, since they have proven to be an effective tool to provide near-optimal solutions in acceptable execution times. Among the vast variety of metaheuristics available in the literature, Differential Evolution (DE) is arguably one of the most popular and successful evolutionary algorithms due to its simplicity and effectiveness. In this paper, the use of DE is analyzed for solving the portfolio optimization problem in electricity markets. Moreover, the performance of DE is compared with another powerful metaheuristic, the Particle Swarm optimization (PSO), showing that despite both algorithms provide good results for the problem, DE overcomes PSO in terms of quality of the solutions.
- Dynamic electricity tariff definition based on market price, consumption and renewable generation patternsPublication . Ribeiro, Catarina; Pinto, Tiago; Faria, Pedro; Ramos, Sérgio Filipe Carvalho; Vale, Zita; Baptista, Jose; Soares, Jono; Navarro-Caceres, Maria; Corchado, Juan ManuelThe increasing use of renewable energy sources and distributed generation brought deep changes in power systems, namely with the operation of competitive electricity markets. With the eminent implementation of micro grids and smart grids, new business models able to cope with the new opportunities are being developed. Virtual Power Players are a new type of player, which allows aggregating a diversity of entities, e.g. generation, storage, electric vehicles, and consumers, to facilitate their participation in the electricity markets and to provide a set of new services promoting generation and consumption efficiency, while improving players' benefits. In order to achieve this objective, it is necessary to define tariff structures that benefit or penalize agents according to their behavior. In this paper a method for determining the tariff structures has been proposed, optimized for different load regimes. Daily dynamic tariff structures were defined and proposed, on an hourly basis, 24 hours day-ahead from the characterization of the typical load profile, the value of the electricity market price and considering the renewable energy production.
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