Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/14567
Título: Analysis and visualization of energy use for university campus
Autor: Abdelkareem, Nourhan Khalifa
Orientador: Casteleyn, Sven
Ribeiro, Sara
Jones, Jim
Palavras-chave: Greenhouse gas emission
Building efficiency
Building conservation
Energy consumption prediction
Data Mining
Artificial neural network
Stepwise multiple regression
Geographical Information Systems
Energy feed back
Data de Defesa: 4-Mar-2015
Resumo: 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.
URI: http://hdl.handle.net/10362/14567
Designação: Master of Science in Geospatial Technologies - TGEO
Aparece nas colecções:NIMS - MSc Dissertations Geospatial Technologies (Erasmus-Mundus)

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