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- 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.