Detalhes
Nome
Paulo Moura OliveiraCargo
Investigador SéniorDesde
01 junho 2012
Nacionalidade
PortugalCentro
Centro de Robótica Industrial e Sistemas InteligentesContactos
+351220413317
paulo.moura.oliveira@inesctec.pt
2024
Autores
Schneider, S; Parada, E; Sengl, D; Baptista, J; Oliveira, PM;
Publicação
FRONTIERS IN SUSTAINABLE CITIES
Abstract
Despite the ubiquitous term climate neutral cities, there is a distinct lack of quantifiable and meaningful municipal decarbonization goals in terms of the targeted energy balance and composition that collectively connect to national scenarios. In this paper we present a simple but useful allocation approach to derive municipal targets for energy demand reduction and renewable expansion based on national energy transition strategies in combination with local potential estimators. The allocation uses local and regional potential estimates for demand reduction and the expansion of renewables and differentiates resulting municipal needs of action accordingly. The resulting targets are visualized and opened as a decision support system (DSS) on a web-platform to facilitate the discussion on effort sharing and potential realization in the decarbonization of society. With the proposed framework, different national scenarios, and their implications for municipal needs for action can be compared and their implications made explicit.
2024
Autores
Teixeira, FL; Soares, SP; Abreu, JP; Oliveira, PM; Teixeira, JP;
Publicação
Optimization, Learning Algorithms and Applications
Abstract
2024
Autores
Rebelo, PM; Lima, J; Soares, SP; Moura Oliveira, P; Sobreira, H; Costa, P;
Publicação
Sensors
Abstract
2023
Autores
Vrancic, D; Oliveira, PM; Bistak, P; Huba, M;
Publicação
MATHEMATICS
Abstract
The main objective of this work was to develop a tuning method for PID controllers suitable for use in an industrial environment. Therefore, a computationally simple tuning method is presented based on a simple experiment on the process without requiring any input from the user. Essentially, the method matches the closed-loop response to the response obtained in the steady-state change experiment. The proposed method requires no prior knowledge of the process and, in its basic form, only the measurement of the change in the steady state of the process in the manually or automatically performed experiment is needed, which is not limited to step-like process input signals. The user does not need to provide any prior information about the process or any information about the closed-loop behavior. Although the control loop dynamics is not defined by the user, it is still known in advance because it is implicitly defined by the process open-loop response. Therefore, no exaggerated control signal swings are expected when the reference signal changes, which is an advantage in many industrial plants. The presented method was designed to be computationally undemanding and can be easily implemented on less powerful hardware, such as lower-end PLC controllers. The work has shown that the proposed model-free method is relatively insensitive to process output noise. Another advantage of the proposed tuning method is that it automatically handles the tuning of highly delayed processes, since the method discards the initial process response. The simplicity and efficiency of the tuning method is demonstrated on several process models and on a laboratory thermal system. The method was also compared to a tuning method based on a similar closed-loop criterion. In addition, all necessary Matlab/Octave files for the calculation of the controller parameters are provided online.
2023
Autores
Pereira, SD; Pires, EJS; Oliveira, PBD;
Publicação
ALGORITHMS
Abstract
A new algorithm based on the ant colony optimization (ACO) method for the multiple traveling salesman problem (mTSP) is presented and defined as ACO-BmTSP. This paper addresses the problem of solving the mTSP while considering several salesmen and keeping both the total travel cost at the minimum and the tours balanced. Eleven different problems with several variants were analyzed to validate the method. The 20 variants considered three to twenty salesmen regarding 11 to 783 cities. The results were compared with best-known solutions (BKSs) in the literature. Computational experiments showed that a total of eight final results were better than those of the BKSs, and the others were quite promising, showing that with few adaptations, it will be possible to obtain better results than those of the BKSs. Although the ACO metaheuristic does not guarantee that the best solution will be found, it is essential in problems with non-deterministic polynomial time complexity resolution or when used as an initial bound solution in an integer programming formulation. Computational experiments on a wide range of benchmark problems within an acceptable time limit showed that compared with four existing algorithms, the proposed algorithm presented better results for several problems than the other algorithms did.
Teses supervisionadas
2022
Autor
Milene Sofia Alves Fraga
Instituição
UTAD
2022
Autor
Tiago Filipe dos Santos
Instituição
UP-FCUP
2022
Autor
José Miguel de Oliveira Bastos
Instituição
UP-FEUP
2022
Autor
José Miguel Ferreira Azevedo Matos
Instituição
UP-FCUP
2022
Autor
Pedro Alexandre santos Letra
Instituição
UTAD
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