Analysis and visualization of energy use for university campus

The reduction of greenhouse gas emissions is one of the big global challenges for the next decades due to its severe impact on the atmosphere that leads to a change in the climate and other environmental factors. One of the main sources of greenhouse gas is energy consumption, therefore a number of initiatives and calls for awareness and sustainability in energy use are issued among different types of institutional and organizations. The European Council adopted in 2007 energy and climate change objectives for 20% improvement until 2020. All European countries are required to use energy with more efficiency. Several steps could be conducted for energy reduction: understanding the buildings behavior through time, revealing the factors that influence the consumption, applying the right measurement for reduction and sustainability, visualizing the hidden connection between our daily habits impacts on the natural world and promoting to more sustainable life. Researchers have suggested that feedback visualization can effectively encourage conservation with energy reduction rate of 18%. Furthermore, researchers have contributed to the identification process of a set of factors which are very likely to influence consumption. Such as occupancy level, occupants behavior, environmental conditions, building thermal envelope, climate zones, etc. Nowadays, the amount of energy consumption at the university campuses are huge and it needs great effort to meet the reduction requested by European Council as well as the cost reduction. Thus, the present study was performed on the university buildings as a use case to: a. Investigate the most dynamic influence factors on energy consumption in campus; b. Implement prediction model for electricity consumption using different techniques, such as the traditional regression way and the alternative machine learning techniques; and c. Assist energy management by providing a real time energy feedback and visualization in campus for more awareness and better decision making. This methodology is implemented to the use case of University Jaume I (UJI), located in Castellon, Spain.

Study of a 9th century silver earrings set from Mikulčice: corrosion, conservation and maintenance

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para obtenção do grau de Mestre em Conservação e Restauro

Determinants of venture capital and private equity investments in renewable energy: A cross-country study

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Economics from the NOVA – School of Business and Economics

A DSL for querying building data streams of energy, eeather and occupation data

The superfluous consumption of energy is faced by the modern society as a Socio-Economical and Environmental problem of the present days. This situation is worsening given that it is becoming clear that the tendency is to increase energy price every year. It is also noticeable that people, not necessarily proficient in technology, are not able to know where savings can be achieved, due to the absence of accessible awareness mechanisms. One of the home user concerns is to balance the need of reducing energy consumption, while producing the same activity with all the comfort and work efficiency. The common techniques to reduce the consumption are to use a less wasteful equipment, altering the equipment program to a more economical one or disconnecting appliances that are not necessary at the moment. However, there is no direct feedback from this performed actions, which leads to the situation where the user is not aware of the influence that these techniques have in the electrical bill. With the intension to give some control over the home consumption, Energy Management Systems (EMS) were developed. These systems allow the access to the consumption information and help understanding the energy waste. However, some studies have proven that these systems have a clear mismatch between the information that is presented and the one the user finds useful for his daily life, leading to demotivation of use. In order to create a solution more oriented towards the user’s demands, a specially tailored language (DSL) was implemented. This solution allows the user to acquire the information he considers useful, through the construction of questions about his energy consumption. The development of this language, following the Model Driven Development (MDD) approach, took into consideration the ideas of facility managers and home users in the phases of design and validation. These opinions were gathered through meetings with experts and a survey, which was conducted to the purpose of collecting statistics about what home users want to know.

Starburst accelerator and incubator construction & energy: How to monitor the activities of an incubator and give strategic advice to startups

This work project takes the format of a problem-solving approach suggested by “Impulse Partners”, a French consulting firm. It recommends a way to measure the success of ten startups in the incubators “Starburst” and “Incubator Construction & Energy” and give them strategic guidance. A Balanced Scorecard is elaborated to help incubated startups building up and implementing their strategy. The Scorecard suggests possible initiatives for the startups that are ideally realized with the help of the consulting firm that is steering the incubator. Special attention is drawn to what the startups need to succeed, which typically concerns financing, networking and managing skills.

Historic and potential technology transition paths of grid battery storage: Co-evolution of energy grid, electric mobility and batteries

Scarcity of fuels, changes in environmental policy and in society increased the interest in generating electric energy from renewable energy sources (RES) for a sustainable energy supply in the future. The main problem of RES as solar and wind energy, which represent a main pillar of this transition, is that they cannot supply constant power output. This results inter alia in an increased demand of backup technologies as batteries to assure electricity system safety. The diffusion of energy storage technologies is highly dependent on the energy system and transport transition pathways which might lead to a replacement or reconfiguration of embedded socio-technical practices and regimes (by creating new standards or dominant designs, changing regulations, infrastructure and user patterns). The success of this technology is dependent on hardly predictable future technical advances, actor preferences, development of competing technologies and designs, diverging interests of actors, future cost efficiencies, environmental performance, the evolution of market demand and design and evolution of our society.

The effect of energy and traffic light labeling on parents purchasing decisions of cereals for their children

Objective: Nutritional labeling systems are considered a tool to fight obesity since they aim to contribute for more informed food choices as well as assist consumers to make healthier nutrition options and in this manner, contribute to a decrease in the obesity rate. This study intends to analyze the effect of different types of labeling systems on parents’ purchasing decisions for their children on a specific product: breakfast cereals. More precisely, how labels affect parents’ perception of healthiness regarding cereals and if the nutritional information has an effect on intended purchases for their children. Participants and methods: We conducted a study with 135 Portuguese parents of children aged 4 to12 years. Parents answered a questionnaire with one of three hypothetical cereals menus. Menus only differed in their nutritional labeling technique: no labels (control group), reference intake labels or traffic light labels. In addition, we conducted 20 face-to-face interviews to a different group of parents in order to perform a recall task. Findings: This paper provides no evidence to suggest that energy labeling or traffic light labeling systems alone were successful in helping parents making healthy purchases of cereals for their children. Therefore, there is the need to promote supplementary policies to encourage the consumption of healthier food and help fight obesity.

Environmental impact assessment of energy crops cultivation in the Mediterranean Europe

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa Para a obtenção do Grau de Mestre em Energia e Bioenergia

Treatment of a nineteenth century male portrait in oil including the characterisation of materials, technique and a study of the lead soap aggregation in the paint composite

Dissertação para obtenção do Grau de Mestre em Conservação e Restauro

Context-based identification of energy consumption in industrial plants

Nowadays, reducing energy consumption is one of the highest priorities and biggest challenges faced worldwide and in particular in the industrial sector. Given the increasing trend of consumption and the current economical crisis, identifying cost reductions on the most energy-intensive sectors has become one of the main concerns among companies and researchers. Particularly in industrial environments, energy consumption is affected by several factors, namely production factors(e.g. equipments), human (e.g. operators experience), environmental (e.g. temperature), among others, which influence the way of how energy is used across the plant. Therefore, several approaches for identifying consumption causes have been suggested and discussed. However, the existing methods only provide guidelines for energy consumption and have shown difficulties in explaining certain energy consumption patterns due to the lack of structure to incorporate context influence, hence are not able to track down the causes of consumption to a process level, where optimization measures can actually take place. This dissertation proposes a new approach to tackle this issue, by on-line estimation of context-based energy consumption models, which are able to map operating context to consumption patterns. Context identification is performed by regression tree algorithms. Energy consumption estimation is achieved by means of a multi-model architecture using multiple RLS algorithms, locally estimated for each operating context. Lastly, the proposed approach is applied to a real cement plant grinding circuit. Experimental results prove the viability of the overall system, regarding both automatic context identification and energy consumption estimation.