Abstract

Abstract

Abstract
Social networks as Facebook and Twitter gained a remarkable attention in the last decade. A huge amount of data is emerging and posted everyday by users that are becoming more interested in and relying on social network for information, news and opinions. Real time posting came to rise and turned easier to report news and events. However, due to its dimensions, in this work we focus on building a system architecture capable of detecting journalistic relevance of posts automatically on this 'haystack' full of data. More specifically, users will have the change to interact with a 'friendly user interface' which will provide several tools to analyze data. © 2017.

Abstract
Technology is a powerful and resourceful tool in teaching and learning as it entices and helps all the parties involved, transforming all activities into alluring or challenging experiences, opening new perspectives into the process and maximizing the availability and use of the tools which the same technology provides. It allows levels of effectiveness in this modern and global era. This study integrates technology in the learning process and evaluation of its various outcomes.The present paper deals with the planning and execution of a lighting design project to be implemented in several monuments and has as main objective the creation and/or replacement of the existing light fixtures with more efficient ones. The lighting systems are intended to illuminate monuments in order to ensure adequate visibility and comfort. The luminous efficiency of a luminary has a dominant influence on the energy consumption and, consequently, in maintenance costs and use of lighting.The project involved the lighting design of monuments or buildings considered relevant or of public interest with the purpose of enhancing their beauty, making them more attractive to the onlookers. A reinforcement of learning applied to the historical monuments lighting configurations will be described. The aim of the projects is also to demonstrate that LED technology allows for greater energetic efficiency in lighting design of monuments. The inherent advantages of replacing the existing solution with one that provides better energy performance will lead to a future reduction of carbon dioxide emissions (CO2), creating an environmentally friendly installation.The challenge was to design a computer aided lighting project using the problem based learning model. With software such as the Dialux computer program, it is possible to analyze the advantage of using correct floodlighting and to understand how software tools can be useful in lighting engineering projects. The simulation design to enhance the authenticity and application of learning derived from the lighting project used to promote skills and understanding of working collaboratively. Both students and teachers shared responsibility in keeping up to date with the products of learning resulting from the simulation project. © 2017 IEEE.

Abstract
Reliable consumption forecasts are crucial in several aspects of power and energy systems, e.g. to take advantage of the full potential of flexibility from consumers and to support the management from operators. With this need, several methodologies for electricity forecasting have emerged. However, the study of correlated external variables, such as temperature or luminosity, is still far from adequate. This paper presents the application of the Wang and Mendel's Fuzzy Rule Learning Method (WM) to forecast electricity consumption. The proposed approach includes two distinct strategies, the first one uses only the electricity consumption as the input of the method, and the second strategy considers a combination of the electricity consumption and the environmental temperature as the input, in order to extract value from the correlation between the two variables. A case study that considers the forecast of the energy consumption of a real office building is also presented. Results show that the WM method using the combination of energy consumption data and environmental temperature is able to provide more reliable forecasts for the energy consumption than several other methods experimented before, namely based on artificial neural networks and support vector machines. Additionally, the WM approach that considers the combination of input values achieves better results than the strategy that considers only the consumption history, hence concluding that WM is appropriate to incorporate different information sources.

Abstract