Using Typical Consumption Profiles to Stablish the Consumption Level for Short Time Periods

The current practices in the consumption metering by electricity utilities is currently largely based on monthly consumption reading. The consumption metering device is always calculating the cumulative consumption. Then, it is possible to calculate the difference between the actual and the previous consumption evaluation in order to estimate the monthly consumption. The power systems planning needs in many aspects to handle consumption data obtained for shorter periods, namely in the Demand Response programs planning. The work presented in this paper is based on the application of typical consumption profiles that are previously defined for a certain power system area. Such profiles are then used in order to estimate the 15 minutes consumption for a certain consumer or consumer type.

A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Consumption Management of Air Conditioning Devices for the Participation in Demand Response Programs

Demand Response has been taking over the years an extreme importance. There’s a lot of demand response programs, one of them proposed in this paper, using air conditioners that could increase the power quality and decrease the spent money in many ways like: infrastructures and customers energy bill reduction. This paper proposes a method and a study on how air conditioners could integrate demand response programs. The proposed method has been modelled as an energy resources management optimization problem. This paper presents two case studies, the first one with all costumers participating and second one with some of costumers. The results obtained for both case studies have been analyzed.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.

Contextual and Environmental Awareness Laboratory for Energy Consumption Management

The recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.

A EUROPA NOS MEDIA – ESTUDO DE CASO SOBRE A EUROPEIZAÇÃO DA ESFERA PÚBLICA PORTUGUESA

Dissertação de Mestrado em Ciência Política e Relações Internacionais – Área de Especialização em Estudos Europeus

Establishing a high titer transient gene expression process in conditioned media for CHO-DG44 cells

Dissertação para obtenção do Grau de Mestre em Biotecnologia

Recommending media content based on machine learning methods

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Brokerage Platform for Media Content Recommendation

Near real time media content personalisation is nowadays a major challenge involving media content sources, distributors and viewers. This paper describes an approach to seamless recommendation, negotiation and transaction of personalised media content. It adopts an integrated view of the problem by proposing, on the business-to-business (B2B) side, a brokerage platform to negotiate the media items on behalf of the media content distributors and sources, providing viewers, on the business-to-consumer (B2C) side, with a personalised electronic programme guide (EPG) containing the set of recommended items after negotiation. In this setup, when a viewer connects, the distributor looks up and invites sources to negotiate the contents of the viewer personal EPG. The proposed multi-agent brokerage platform is structured in four layers, modelling the registration, service agreement, partner lookup, invitation as well as item recommendation, negotiation and transaction stages of the B2B processes. The recommendation service is a rule-based switch hybrid filter, including six collaborative and two content-based filters. The rule-based system selects, at runtime, the filter(s) to apply as well as the final set of recommendations to present. The filter selection is based on the data available, ranging from the history of items watched to the ratings and/or tags assigned to the items by the viewer. Additionally, this module implements (i) a novel item stereotype to represent newly arrived items, (ii) a standard user stereotype for new users, (iii) a novel passive user tag cloud stereotype for socially passive users, and (iv) a new content-based filter named the collinearity and proximity similarity (CPS). At the end of the paper, we present off-line results and a case study describing how the recommendation service works. The proposed system provides, to our knowledge, an excellent holistic solution to the problem of recommending multimedia contents.

Social Media Integration in Video Games: A Social Overlay for Desktop Games

Simpósio de Informática (INForum 2015), Covilhã, Portugal. Notes: Best paper award nominee.

L'"errore di erode" e la media in Giovanni da Parigi

pp. 169-184

Ex-ante evaluation by the media of the Guimarães 2012 European Capital of Culture

The nomination of Guimarães to host the 2012 European Capital of Culture (ECC) has put on the agenda of the city the need of measuring the effects that the implementation of this mega event could have in it and in the municipality a whole. The balance of the benefits and costs and an extended community involvement tend to reduce negative impacts and enhance positive ones. This chapter analyzes the involvement of population and local associations in the planning and organization of the 2012 Guimarães European Capital of Culture, using the coverage made during 2011 by local and national press of the mega event. A content analysis of the news published covering the period between January and December 2011 and using three newspapers was conducted. From those, two were local and weekly newspapers and one was a national daily one. Looking to data results, it can be concluded that it was poor the community involvement and, also, the one of the cultural associations in the organizations of the 2012 ECC. A strong negative reaction to the model choose to plan the mega event conducted by official organizers was found, which has cast doubts on the desirable participation of the residents and, consequently, on the success of the mega event, especially in a perspective of a medium and long term effects.

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse.

Comparison among tomato juice agar with other three media for differentiation of Candida dubliniensis from Candida albicans

The purpose of the present study is to compare the tomato juice agar, a well known medium employed to observe ascospore formation, with niger seed agar, casein agar and sunflower seed agar, applied to a differentiation between C. dubliniensis and C. albicans. After 48 hours of incubation at 30 ºC all 26 (100%) C. dubliniensis isolates tested produced chlamydospores on tomato juice agar as well as in the other three media evaluated. However, when we inoculated all media with C. albicans, the absence of chlamydospores became resulting in the following percents: tomato juice agar (92.47%), niger seed agar (96.7%), casein agar (91.39%), and sunflower seed agar (96.7%). These results indicate that tomato juice agar is another medium which can also be used in the first phenotypic differentiation between C. dubliniensis and C. albicans.

The Media Online and the Climate Change

Dissertação apresentada para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Novos Media e Práticas Web