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Authors
Abstract(s)
Nos últimos anos o consumo de energia elétrica produzida a partir de fontes renováveis tem
aumentado significativamente. Este aumento deve-se ao impacto ambiental que recursos como
o petróleo, gás, urânio, carvão, entre outros, têm no meio ambiente e que são notáveis no diaa-
dia com as alterações climáticas e o aquecimento global. Por sua vez, estes recursos têm um
ciclo de vida limitado e a dada altura tornar-se-ão escassos.
A preocupação de uma melhoria contínua na redução dos impactos ambientais levou à criação
de Normas para uma gestão mais eficiente e sustentável do consumo de energia nos edifícios.
Parte da eletricidade vendida pelas empresas de comercialização é produzida através de fontes
renováveis, e com a recente publicação do Decreto de Lei nº 153/2014 de 20 outubro de 2014
que regulamenta o autoconsumo, permitindo que também os consumidores possam produzir
a sua própria energia nas suas residências para reduzir os custos com a compra de eletricidade.
Neste contexto surgiram os edifícios inteligentes. Por edifícios inteligentes entende-se que são
edifícios construídos com materiais que os tornam mais eficientes, possuem iluminação e
equipamentos elétricos mais eficientes, e têm sistemas de produção de energia que permitem
alimentar o próprio edifício, para um consumo mais sustentado. Os sistemas implementados
nos edifícios inteligentes visam a monitorização e gestão da energia consumida e produzida
para evitar desperdícios de consumo.
O trabalho desenvolvido visa o estudo e a implementação de Redes Neuronais Artificiais (RNA)
para prever os consumos de energia elétrica dos edifícios N e I do ISEP/GECAD, bem como a
previsão da produção dos seus painéis fotovoltáicos. O estudo feito aos dados de consumo
permitiu identificar perfis típicos de consumo ao longo de uma semana e de que forma são
influenciados pelo contexto, nomeadamente, com os dias da semana versus fim-de-semana, e
com as estações do ano, sendo analisados perfis de consumo de inverno e verão. A produção
de energia através de painéis fotovoltaicos foi também analisada para perceber se a produção
atual é suficiente para satisfazer as necessidades de consumo dos edifícios. Também foi
analisada a possibilidade da produção satisfazer parcialmente as necessidades de consumos
específicos, por exemplo, da iluminação dos edifícios, dos seus sistemas de ar condicionado ou
dos equipamentos usados.
In recent years the consumption of electricity produced from renewable sources has increased significantly. This increase is due to the environmental impact that resources such as oil, gas, uranium, coal, among others, have on the environment and that are noticeable in day-to-day climate change and global warming. In turn, these resources have a limited life cycle and at some point will become scarce. The concern of continuous improvement in reducing environmental impacts led to the creation of standards for more efficient and sustainable management of energy consumption in buildings. Part of the electricity sold by marketing companies is produced from renewable sources, and with the recent publication of Decree Law No. 153/2014 of 20 October 2014 which regulates the self-consumption, so consumers can produce their own energy and thus reduce the cost of buying electricity. In this context the concept of intelligent buildings has arised. Intelligent buildings are constructed with materials which make them more efficient, have lighting and electrical equipment more efficient, and have power generation systems that allow to feed the own building, for a more sustained consumption. The systems implemented in the intelligent building aimed at monitoring and manage consumption and production to optimize the use of the resources. This work aims to study and implement Artificial Neural Networks (ANN) to predict the energy consumption of buildings N and I of ISEP / GECAD, and to forecast the production of their photovoltaic panels. The analysis of consumption data indicate typical consumption profiles over a week and is influenced with the seasons, being analyzed winter and summer consumption profiles. The production of energy through photovoltaic panels was also analyzed to see if the current production is sufficient to meet the energy needs of the buildings. The study if how the production could, even partially, met the consumption was also done, for specific types of consumption, namely: lighting, HVAC and other equipment.
In recent years the consumption of electricity produced from renewable sources has increased significantly. This increase is due to the environmental impact that resources such as oil, gas, uranium, coal, among others, have on the environment and that are noticeable in day-to-day climate change and global warming. In turn, these resources have a limited life cycle and at some point will become scarce. The concern of continuous improvement in reducing environmental impacts led to the creation of standards for more efficient and sustainable management of energy consumption in buildings. Part of the electricity sold by marketing companies is produced from renewable sources, and with the recent publication of Decree Law No. 153/2014 of 20 October 2014 which regulates the self-consumption, so consumers can produce their own energy and thus reduce the cost of buying electricity. In this context the concept of intelligent buildings has arised. Intelligent buildings are constructed with materials which make them more efficient, have lighting and electrical equipment more efficient, and have power generation systems that allow to feed the own building, for a more sustained consumption. The systems implemented in the intelligent building aimed at monitoring and manage consumption and production to optimize the use of the resources. This work aims to study and implement Artificial Neural Networks (ANN) to predict the energy consumption of buildings N and I of ISEP / GECAD, and to forecast the production of their photovoltaic panels. The analysis of consumption data indicate typical consumption profiles over a week and is influenced with the seasons, being analyzed winter and summer consumption profiles. The production of energy through photovoltaic panels was also analyzed to see if the current production is sufficient to meet the energy needs of the buildings. The study if how the production could, even partially, met the consumption was also done, for specific types of consumption, namely: lighting, HVAC and other equipment.
Description
Keywords
Energia elétrica Energias renováveis Edifícios inteligentes Sustentabilidade Eficiência energética Previsão Electricity Renewable energy Intelligent buildings Sustainability Energy efficiency Prediction