Browsing by Author "Chamoso, Pablo"
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- A Case Study for a Smart City Energy Management ResourcesPublication . Canizes, Bruno; Pinto, Tiago; Soares, João; Vale, Zita; Chamoso, Pablo; Santos, DanielA physical smart city model environment is used to presents the demonstration of an energy resources management approach. The demand for smart cities has been created by several factors from the governments, society and industry. Thus, smart grids focus on the intelligent management of energy resources in order to maximize the usage of the energy from renewable sources in order to the final consumers feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This paper presents a realistic and physical experimentation of the energy resource management. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.
- 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
- Smart City: A GECAD-BISITE Energy Management Case StudyPublication . Canizes, Bruno; Pinto, Tiago; Soares, João; Vale, Zita; Chamoso, Pablo; Santos, DanielThis paper presents the demonstration of an energy resources management approach using a physical smart city model environment. Several factors from the industry, governments and society are creating the demand for smart cities. In this scope, smart grids focus on the intelligent management of energy resources in a way that the use of energy from renewable sources can be maximized, and that the final consumers can feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This work thus presents an innovative means to enable a realistic, physical, experimentation of the impacts of novel energy resource management models, without affecting consumers. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles.
- Solar Thermal Collector Output Temperature Prediction by Hybrid Intelligent Model for Smartgrid and Smartbuildings Applications and OptimizationPublication . Casteleiro-Roca, José-Luis; Chamoso, Pablo; Jove, Esteban; González-Briones, Alfonso; Quintián, Héctor; Fernández-Ibáñez, María-Isabel; Vega Vega, Rafael Alejandro; Piñón Pazos, Andrés-José; López Vázquez, José Antonio; Torres-Álvarez, Santiago; Pinto, Tiago; Calvo-Rolle, Jose LuisCurrently, there is great interest in reducing the consumption of fossil fuels (and other non-renewable energy sources) in order to preserve the environment; smart buildings are commonly proposed for this purpose as they are capable of producing their own energy and using it optimally. However, at times, solar energy is not able to supply the energy demand fully; it is mandatory to know the quantity of energy needed to optimize the system. This research focuses on the prediction of output temperature from a solar thermal collector. The aim is to measure solar thermal energy and optimize the energy system of a house (or building). The dataset used in this research has been taken from a real installation in a bio-climate house located on the Sotavento Experimental Wind Farm, in north-west Spain. A hybrid intelligent model has been developed by combining clustering and regression methods such as neural networks, polynomial regression, and support vector machines. The main findings show that, by dividing the dataset into small clusters on the basis of similarity in behavior, it is possible to create more accurate models. Moreover, combining different regression methods for each cluster provides better results than when a global model of the whole dataset is used. In temperature prediction, mean absolute error was lower than 4 ∘ C.