Today,there is a growing interest in developing energy efficient buildings since it is estimated that buildings account for about 40%of the total primary energy consumption in the world.In relation to existing buildin...Today,there is a growing interest in developing energy efficient buildings since it is estimated that buildings account for about 40%of the total primary energy consumption in the world.In relation to existing buildings,energy efficiency retrofits have become an important opportunity to upgrade the energy performance of commercial,public and residential buildings that may reduce the energy consumption,demand and cost.In this paper we cover the energy efficiency deep retrofit process that has been carried out for Nottingham Playhouse theatre building for the aim of enhancing its environmental performance and analysing the energy efficiency gained after implementing certain proposed modifications.It is a nationally protected historic building,listed as Grade II*on The National Heritage List for England(NHLE).The building has had insulation enhancement,doors modifications,solar energy installations,energy-saving lights,in addition to improved heating and air conditioning system.The paper presents a novel methodology;and its results indicate significant improvements in the building’s energy performance which is demonstrated using infrared thermographic images and data logger sensors where significant energy savings to the building’s thermal performance are obtained.The energy saving measures have been completed while maintaining the heritage building’s general appearance and architectural features,which have received a Commendation Certificate from The Nottingham Civic Society for this achievement.展开更多
In most countries,buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use.To reduce this massive consumption and decreas...In most countries,buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use.To reduce this massive consumption and decrease carbon emission,thermal insulation of buildings can play an important role.The estimation of energy savings following the improvement of a building’s insulation remains a key area of research in order to calculate the cost savings and the payback period.In this paper,a case study has been presented where deep retrofitting has been introduced to an existing building to bring it closer to a Passivhaus standard with the introduction of insulation and solar photovoltaic panels.The thermal performance of the building with its improved insulation has been evaluated using infrared thermography.Artificial intelligence using deep learning neural networks is implemented to predict the thermal performance of the building and the expected energy savings.The prediction of neural networks is compared with the actual savings calculated using historical weather data.The results of the neural network show high accuracy of predicting the actual energy savings with success rate of about 82%when compared with the calculated values.The results show that this suggested approach can be used to rapidly predict energy savings from retrofitting of buildings with reasonable accuracy,hence providing a practical rapid tool for the building industry and communities to estimate energy savings.A mathematical model has been also developed which has indicated a life-long monitoring will be needed to precisely estimate the benefits of energy savings in retrofitting due to the change in weather conditions and people’s behaviour.展开更多
文摘Today,there is a growing interest in developing energy efficient buildings since it is estimated that buildings account for about 40%of the total primary energy consumption in the world.In relation to existing buildings,energy efficiency retrofits have become an important opportunity to upgrade the energy performance of commercial,public and residential buildings that may reduce the energy consumption,demand and cost.In this paper we cover the energy efficiency deep retrofit process that has been carried out for Nottingham Playhouse theatre building for the aim of enhancing its environmental performance and analysing the energy efficiency gained after implementing certain proposed modifications.It is a nationally protected historic building,listed as Grade II*on The National Heritage List for England(NHLE).The building has had insulation enhancement,doors modifications,solar energy installations,energy-saving lights,in addition to improved heating and air conditioning system.The paper presents a novel methodology;and its results indicate significant improvements in the building’s energy performance which is demonstrated using infrared thermographic images and data logger sensors where significant energy savings to the building’s thermal performance are obtained.The energy saving measures have been completed while maintaining the heritage building’s general appearance and architectural features,which have received a Commendation Certificate from The Nottingham Civic Society for this achievement.
文摘In most countries,buildings are responsible for significant energy consumption where space heating and air conditioning is responsible for the majority of this energy use.To reduce this massive consumption and decrease carbon emission,thermal insulation of buildings can play an important role.The estimation of energy savings following the improvement of a building’s insulation remains a key area of research in order to calculate the cost savings and the payback period.In this paper,a case study has been presented where deep retrofitting has been introduced to an existing building to bring it closer to a Passivhaus standard with the introduction of insulation and solar photovoltaic panels.The thermal performance of the building with its improved insulation has been evaluated using infrared thermography.Artificial intelligence using deep learning neural networks is implemented to predict the thermal performance of the building and the expected energy savings.The prediction of neural networks is compared with the actual savings calculated using historical weather data.The results of the neural network show high accuracy of predicting the actual energy savings with success rate of about 82%when compared with the calculated values.The results show that this suggested approach can be used to rapidly predict energy savings from retrofitting of buildings with reasonable accuracy,hence providing a practical rapid tool for the building industry and communities to estimate energy savings.A mathematical model has been also developed which has indicated a life-long monitoring will be needed to precisely estimate the benefits of energy savings in retrofitting due to the change in weather conditions and people’s behaviour.
