To explore the energy saving effect of building envelope, the experiments were carried out through a comparison of basic cubicle in summer. Experiments show that if energy efficiency measures are applied only in the e...To explore the energy saving effect of building envelope, the experiments were carried out through a comparison of basic cubicle in summer. Experiments show that if energy efficiency measures are applied only in the external walls and windows, the energy saving cubicles have an average energy efficiency ratio of 27.75% and 27.05% when the air change rates are 1.1 and 1.4 h-1 in summer, with both values being over the standard target value by 25%. And the indoor air temperature of the energy saving cubicle is below that of the basic cubicle. The daily mean temperature difference between the interior surface of insulation wall and no insulation reaches 1.47℃, and the mean temperature difference is up to 8.52℃ between the interior surface and exterior surface of insulating glass and single glass. The two cubicles were simulated for energy consumption using VisualDOE4.0 software under real weather conditions in summer. The results show that the mean deviation is 10.02% between experimental and simulated energy efficiency ratio. The correctness and validity of simulation results of the VisualDOE4.0 software are proved.展开更多
This paper reviews the major North American and Australian sustainability rating tools to determine how they measure building energy performance.It then reviews the major building energy simulation software packages.T...This paper reviews the major North American and Australian sustainability rating tools to determine how they measure building energy performance.It then reviews the major building energy simulation software packages.The paper then details some of the literature surrounding predicted vs.actual energy performance in green buildings,and concludes with an argument for a more performance-orientated ratings regime.展开更多
The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing bui...The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system,which can be subdivided into three aspects:(i) building category;(i i) region category; and(iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating(DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics.To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system(i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.展开更多
To begin with, rating systems are a beneficial tool in determining the efficiency of a building’s ability to utilise its resources effectively. In this study, the two elements under comparison are the Building Rating...To begin with, rating systems are a beneficial tool in determining the efficiency of a building’s ability to utilise its resources effectively. In this study, the two elements under comparison are the Building Rating Systems (BRSs) and Occupant Rating Systems (ORSs). The main objective of this paper is to be able to examine the most commonly applied international and national BRS and ORS and, based on that, discover the possibility of developing an integration of both the BRS and ORS into one rating system. Quite simply, a BRS is a method by which buildings are assessed and given a score based on numerous features such as the efficiency of each of the services, total energy consumption, and alternate options of consumption. There are various BRSs that are implemented globally, each with its own set of criteria and specifications. Thus, based on the analysis of the benefits and drawbacks of both types of rating systems, it could be deduced that a well-rounded rating system with all technical and non-technical aspects combined would be beneficial to both the efficiency of the building as well as the building occupants’ health and well-being.展开更多
This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinea...This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.展开更多
Improving daylighting strategy is a mandatory step to achieve visual enjoyment and energy saving in buildings. Psycho, physiological effects and energy performance have to be investigated in order to define a range of...Improving daylighting strategy is a mandatory step to achieve visual enjoyment and energy saving in buildings. Psycho, physiological effects and energy performance have to be investigated in order to define a range of different daylighting strategies, thanks to daylighting devices and climate based daylight modeling. Daylighting optimization ensures indoor healthier rooms, reduces electric light consumption and cuts the risk of glare. The best way to achieve these targets is to define users lighting needs, based on visual targets and to draw up some green measures to reduce electricity demands. Involving new climate-based daylight modeling metrics aims at defining proper illumination targets, in order to drastically reduce electrical lights, as well as reducing thermal loads deriving from cooling and HVAC （heating, ventilation and air conditioning） systems.展开更多
Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and ...Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.展开更多
基金Project(2006BAJ01A05) supported by National Science and Technology Pillar Program during the 11th Five-year Plan Period of China
文摘To explore the energy saving effect of building envelope, the experiments were carried out through a comparison of basic cubicle in summer. Experiments show that if energy efficiency measures are applied only in the external walls and windows, the energy saving cubicles have an average energy efficiency ratio of 27.75% and 27.05% when the air change rates are 1.1 and 1.4 h-1 in summer, with both values being over the standard target value by 25%. And the indoor air temperature of the energy saving cubicle is below that of the basic cubicle. The daily mean temperature difference between the interior surface of insulation wall and no insulation reaches 1.47℃, and the mean temperature difference is up to 8.52℃ between the interior surface and exterior surface of insulating glass and single glass. The two cubicles were simulated for energy consumption using VisualDOE4.0 software under real weather conditions in summer. The results show that the mean deviation is 10.02% between experimental and simulated energy efficiency ratio. The correctness and validity of simulation results of the VisualDOE4.0 software are proved.
文摘This paper reviews the major North American and Australian sustainability rating tools to determine how they measure building energy performance.It then reviews the major building energy simulation software packages.The paper then details some of the literature surrounding predicted vs.actual energy performance in green buildings,and concludes with an argument for a more performance-orientated ratings regime.
文摘The operational rating system in building energy performance certificates(EPCs) has been used for systematically monitoring and diagnosing the energy performance in the operation and maintenance phases of existing buildings. However, there are several limitations of the conventional operational rating system,which can be subdivided into three aspects:(i) building category;(i i) region category; and(iii) space unit size. To overcome these challenges, this study conducted the problem analysis of the conventional operational rating system for existing buildings by using the statistical and geostatistical approaches. Based on the problem analysis, this study developed the dynamic operational rating(DOR) system for existing buildings by using the data-mining technique and the probability approach. The developed DOR system can be used as a tool for building energy performance diagnostics.To validate the applicability of the developed DOR system, educational facilities were selected as the representative type of existing buildings in South Korea. As a result, it was determined that the developed DOR system can solve the irrationality of the conventional operational rating system(i.e., the negative correlation between the space unit size and the CO2 emission density). Namely, the operational ratings of small buildings were adjusted upward while those of large buildings were adjusted downward. The developed DOR system can allow policymakers to establish the reasonable operational rating system for existing buildings, which can motivate the public to actively participate in energy-saving campaigns.
文摘To begin with, rating systems are a beneficial tool in determining the efficiency of a building’s ability to utilise its resources effectively. In this study, the two elements under comparison are the Building Rating Systems (BRSs) and Occupant Rating Systems (ORSs). The main objective of this paper is to be able to examine the most commonly applied international and national BRS and ORS and, based on that, discover the possibility of developing an integration of both the BRS and ORS into one rating system. Quite simply, a BRS is a method by which buildings are assessed and given a score based on numerous features such as the efficiency of each of the services, total energy consumption, and alternate options of consumption. There are various BRSs that are implemented globally, each with its own set of criteria and specifications. Thus, based on the analysis of the benefits and drawbacks of both types of rating systems, it could be deduced that a well-rounded rating system with all technical and non-technical aspects combined would be beneficial to both the efficiency of the building as well as the building occupants’ health and well-being.
文摘This study examines the exchange rate pass-through to the United States(US)restaurant and hotel prices by incorporating the effect of monetary policy uncertainty over the period 2001:M12 to 2019:M01.Using the nonlinear autoregressive distributed lag(NARDL)model,empirical evidence indicates asymmetric pass-through of exchange rate and monetary policy uncertainty.Moreover,a stronger pass-through effect is observed during depreciation and a negative shock in monetary policy uncertainty,corroborating asymmetric pass-through predictions.Our results further show that a positive shock in energy prices leads to an increase in restaurant and hotel prices.Furthermore,asymmetric causality indicates that a positive shock in the exchange rate causes a positive shock to restaurant and hotel prices.We found feedback causal effects between positive and negative shocks in monetary policy uncertainty and positive and negative shocks in the exchange rate.Additionally,we detected a one-way asymmetric causality,flowing from a positive(negative)shock to a positive(negative)shock in energy prices.Therefore,these findings provide insights for policymakers to achieve low and stable prices in the US restaurant and hotel industry through sound monetary policy formulations.Highlights.The drivers of restaurant and hotel business in tourism destinations are examined.There is asymmetric pass-through of exchange rate and monetary policy uncertainty.A stronger pass-through is observed during appreciation and a negative shock to monetary policy uncertainty.There is asymmetric causality from positive shock in exchange rate to postive shock in restaurant and hotel prices.
文摘Improving daylighting strategy is a mandatory step to achieve visual enjoyment and energy saving in buildings. Psycho, physiological effects and energy performance have to be investigated in order to define a range of different daylighting strategies, thanks to daylighting devices and climate based daylight modeling. Daylighting optimization ensures indoor healthier rooms, reduces electric light consumption and cuts the risk of glare. The best way to achieve these targets is to define users lighting needs, based on visual targets and to draw up some green measures to reduce electricity demands. Involving new climate-based daylight modeling metrics aims at defining proper illumination targets, in order to drastically reduce electrical lights, as well as reducing thermal loads deriving from cooling and HVAC （heating, ventilation and air conditioning） systems.
文摘Due to the impact of occupants’activities in buildings,the relationship between electricity demand and ambient temperature will show different trends in the long-term and short-term,which show seasonal variation and hourly variation,respectively.This makes it difficult for conventional data fitting methods to accurately predict the long-term and short-term power demand of buildings at the same time.In order to solve this problem,this paper proposes two approaches for fitting and predicting the electricity demand of office buildings.The first proposed approach splits the electricity demand data into fixed time periods,containing working hours and non-working hours,to reduce the impact of occupants’activities.After finding the most sensitive weather variable to non-working hour electricity demand,the building baseload and occupant activities can be predicted separately.The second proposed approach uses the artificial neural network(ANN)and fuzzy logic techniques to fit the building baseload,peak load,and occupancy rate with multi-variables of weather variables.In this approach,the power demand data is split into a narrower time range as no-occupancy hours,full-occupancy hours,and fuzzy hours between them,in which the occupancy rate is varying depending on the time and weather variables.The proposed approaches are verified by the real data from the University of Glasgow as a case study.The simulation results show that,compared with the traditional ANN method,both proposed approaches have less root-mean-square-error(RMSE)in predicting electricity demand.In addition,the proposed working and non-working hour based regression approach reduces the average RMSE by 35%,while the ANN with fuzzy hours based approach reduces the average RMSE by 42%,comparing with the traditional power demand prediction method.In addition,the second proposed approach can provide more information for building energy management,including the predicted baseload,peak load,and occupancy rate,without requiring additional building parameters.
