Percorrer por autor "Cardeira, Carlos"
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- Enhanced PCA-based localization using depth maps with missing dataPublication . Carreira, Fernando; Calado, João Manuel Ferreira; Cardeira, Carlos; Oliveira, PauloIn this paper a new method for self-localization of mobile robots, based on a PCA positioning sensor to operate in unstructured environments, is proposed and experimentally validated. The proposed PCA extension is able to perform the eigenvectors computation from a set of signals corrupted by missing data. The sensor package considered in this work contains a 2D depth sensor pointed upwards to the ceiling, providing depth images with missing data. The positioning sensor obtained is then integrated in a Linear Parameter Varying mobile robot model to obtain a self-localization system, based on linear Kalman filters, with globally stable position error estimates. A study consisting in adding synthetic random corrupted data to the captured depth images revealed that this extended PCA technique is able to reconstruct the signals, with improved accuracy. The self-localization system obtained is assessed in unstructured environments and the methodologies are validated even in the case of varying illumination conditions.
- Home energy forecast performance tool for smart living services suppliers under an energy 4.0 and CPS frameworkPublication . RODRIGUES, FILIPE; Cardeira, Carlos; Calado, João Manuel Ferreira; Melicio, RuiIndustry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measure ment, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy man agement; well-designed applications will successfully inform, engage, empower, and motivate con sumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), pro vides more reliable forecasting for a few hours ahead and peak loads.
- Navigation system for mobile robots using PCA-based localization from ceiling depth images: experimental validationPublication . Carreira, Fernando; Calado, João Manuel Ferreira; Cardeira, Carlos; Oliveira, P.This paper aims the experimental validation of a mobile robot navigation system, using self-localization based on principal component analysis (PCA) of ceiling depth images. In this approach, a roadmap based on generalized Voronoi diagram (GVD) is built from an occupancy grid, that is defined in the ceiling mapping to the PCA database. The system resorts to the Dijkstra algorithm to planning paths, using the GVD-based roadmap, from which a set of waypoints are extracted. During the mission, the robot is commanded by a controller based on self-located using only the information provided from ceiling depth images and other on-board sensors. The navigation system ensures that the robot reaches its destination, travelling along safety trajectories, while computing its pose with global stable estimates, from Kalman filters (KF). The navigation is achieved without the need to structure the environment, searching by specific features, and to linearize the model. The results are experimentally validated in an indoor environment, using a differential-drive mobile robot.
- Short-term load forecasting of electricity demand for the residential sector based on modelling techniques: A systematic reviewPublication . Rodrigues, Filipe; Cardeira, Carlos; Calado, João; Melício, RuiIn this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.
- The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in PortugalPublication . Rodrigues, Filipe Martins; Cardeira, Carlos; Calado, João Manuel FerreiraIt is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.