Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy...Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.展开更多
Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year.Residential airconditioning is widely believed to be the prime cause of the rise ...Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year.Residential airconditioning is widely believed to be the prime cause of the rise in peak demand but,in the absence of detailed residential demand research,there is no bottom-up empirical evidence to support this supposition or to estimate its impact.This paper first examines the developments in network peak demand,at a national,network distribution,and local distribution feeder level to show recent trends in peak demand.Secondly,this paper applies analytics to the half-hourly consumption data of a sample of Ausgrid’s interval metered customers,combined with local weather data,to develop an algorithm which can recognize air-conditioner use and can identify consumption patterns and peak load.This estimate is then compared to system peaks to determine residential airconditioning’s impact on overall demand.Finally,this paper considers the future impacts of air-conditioning load on peak demand as penetration rates reaches saturation levels and new minimum energy performance standards take effect reducing new units peak impacts.展开更多
基金support and facilities provieded by Bharat Sanchar Nigam Limited Chennai Telephones and Department of Telecommunications,India for this study
文摘Energy saving is one of the most important research hotspots, by which operational expenditure and CO2 emission can be reduced. Optimal cooling capacity scheduling in addition to temperature control can improve energy efficiency. The main contribution of this work is modeling the telecommunication building for the fabric cooling load to schedule the operation of air conditioners. The time series data of the fabric cooling load of the building envelope is taken by simulation by using Energy Plus, Building Control Virtual Test Bed （BCVTB）, and Matlab. This pre-computed data and other internal thermal loads are used for scheduling in air conditioners. Energy savings obtained for the whole year are about 4% to 6% by simulation and the field study, respectively.
基金This work was supported in part by an ARC Grant LP110200957.
文摘Rapid growth in electricity network peak demand is increasing pressure for new investment which may be used for only a few hours a year.Residential airconditioning is widely believed to be the prime cause of the rise in peak demand but,in the absence of detailed residential demand research,there is no bottom-up empirical evidence to support this supposition or to estimate its impact.This paper first examines the developments in network peak demand,at a national,network distribution,and local distribution feeder level to show recent trends in peak demand.Secondly,this paper applies analytics to the half-hourly consumption data of a sample of Ausgrid’s interval metered customers,combined with local weather data,to develop an algorithm which can recognize air-conditioner use and can identify consumption patterns and peak load.This estimate is then compared to system peaks to determine residential airconditioning’s impact on overall demand.Finally,this paper considers the future impacts of air-conditioning load on peak demand as penetration rates reaches saturation levels and new minimum energy performance standards take effect reducing new units peak impacts.
