Shukla P.R.,Indian Institute of Management Ahmedabad |
Chaturvedi V.,5825 University Research Court
Energy Economics | Year: 2012
Low carbon energy technologies are of increasing importance to India for reducing emissions and diversifying its energy supply mix. Using GCAM, an integrated assessment model, this paper analyzes a targets approach for pushing solar, wind, and nuclear technologies in the Indian electricity generation sector from 2005 to 2095. Targets for these technologies have been constructed on the basis of Indian government documents, policy announcements, and expert opinions. Different targets have been set for the reference scenario and the carbon price scenario. In the reference scenario, wind and nuclear technologies exceed respective targets in the long run without any subsidy push, while solar energy requires subsidy push throughout the century in order to meet its high targets. In the short run, nuclear energy also requires significant subsidy, including a much higher initial subsidy relative to solar power, which is a result of its higher targets. Under a carbon price scenario, the carbon price drives the penetration of these technologies. Still, subsidy is required - especially in the short run when the carbon price is low. We also found that pushing solar, wind, and nuclear technologies leads to a decrease in share of CCS under the carbon price scenario and biomass under both the reference and carbon price scenarios. This is because low carbon technologies compete among themselves and substitute each other, thereby enhancing the need for subsidy or carbon price, highlighting that proposed targets are not set at efficient levels. In light of contemporary debate on external costs of nuclear energy, we also assess the sensitivity of the results to nuclear technology cost. We find that higher cost significantly decreases the share of nuclear power under both the reference and carbon price scenarios. © 2012 Elsevier B.V.
Song C.,5825 University Research Court |
Porter A.,University of Maryland University College |
Foster J.S.,University of Maryland University College
IEEE Transactions on Software Engineering | Year: 2014
Modern software systems are increasingly configurable. While this has many benefits, it also makes some software engineering tasks,such as software testing, much harder. This is because, in theory,unique errors could be hiding in any configuration, and, therefore,every configuration may need to undergo expensive testing. As this is generally infeasible, developers need cost-effective technique for selecting which specific configurations they will test. One popular selection approach is combinatorial interaction testing (CIT), where the developer selects a strength $t$ and then computes a covering array (a set of configurations) in which all $t$-way combinations of configuration option settings appear at least once. In prior work, we demonstrated several limitations of the CIT approach. In particular, we found that a given system's effective configuration space-the minimal set of configurations needed to achieve a specific goal-could comprise only a tiny subset of the system's full configuration space. We also found that effective configuration space may not be well approximated by $t$-way covering arrays. Based on these insights we have developed an algorithm called interaction tree discovery (iTree). iTree is an iterative learning algorithm that efficiently searches for a small set of configurations that closely approximates a system's effective configuration space. On each iteration iTree tests the system on a small sample of carefully chosen configurations, monitors the system's behaviors, and then applies machine learning techniques to discover which combinations of option settings are potentially responsible for any newly observed behaviors. This information is used in the next iteration to pick a new sample of configurations that are likely to reveal further new behaviors. In prior work, we presented an initial version of iTree and performed an initial evaluation with promising results. This paper presents an improved iTree algorithm in greater detail. The key improvements are based on our use of composite proto-interactions-a construct that improves iTree's ability to correctly learn key configuration option combinations, which in turn significantly improves iTree's running time, without sacrificing effectiveness. Finally, the paper presents a detailed evaluation of the improved iTree algorithm by comparing the coverage it achieves versus that of covering arrays and randomly generated configuration sets, including a significantly expanded scalability evaluation with the $\sim$1M-LOC MySQL. Our results strongly suggest that the improved iTree algorithm is highly scalable and can identify a high-coverage test set of configurations more effectively than existing methods. © 2013 IEEE.
Arkin P.A.,University of Maryland University College |
Smith T.M.,5825 University Research Court |
Sapiano M.R.P.,Colorado State University |
Janowiak J.,University of Maryland University College
Environmental Research Letters | Year: 2010
Climate models project large changes in global surface temperature in coming decades that are expected to be accompanied by significant changes in the global hydrological cycle. Validation of model simulations is essential to support their use in decision making, but observing the elements of the hydrological cycle is challenging, and model-independent global data sets exist only for precipitation. We compute the sensitivity of the global hydrological cycle to changes in surface temperature using available global precipitation data sets and compare the results against the sensitivities derived from model simulations of 20th century climate. The implications of the results for the global climate observing system are discussed. © 2010 IOP Publishing Ltd Printed in the UK.
Wang P.,Chinese Academy of Meteorological Sciences |
Xie D.,Beijing Normal University |
Zhou Y.,5825 University Research Court |
E Y.,Chinese Academy of Meteorological Sciences |
Zhu Q.,Beijing Normal University
Environmental Earth Sciences | Year: 2014
The ecological structure in the arid and semi-arid region of Northwest China with forest, grassland, agriculture, Gobi, and desert, is complex, vulnerable, and unstable. It is a challenging and sustaining job to keep the ecological structure and improve its ecological function. Net primary productivity (NPP) modeling can help to improve the understanding of the ecosystem, and therefore, improve ecological efficiency. The boreal ecosystem productivity simulator (BEPS) model provides the possibility of NPP modeling in terrestrial ecosystem, but it has some limitations for application in arid and semi-arid regions. In this paper, we improve the BEPS model, in terms of its water cycle by adding the processes of infiltration and surface runoff, to be applicable in arid and semi-arid regions. We model the NPP of forest, grass, and crop in Gansu Province as an experimental area in Northwest China in 2003 using the improved BEPS model, parameterized with moderate resolution remote sensing imageries and meteorological data. The modeled NPP using improved BEPS agrees better with the ground measurements in Qilian Mountain than that with original BEPS, with a higher R2 of 0.746 and lower root mean square error (RMSE) of 46.53 gC m−2 compared to R2 of 0.662 and RMSE of 60.19 gC m−2 from original BEPS. The modeled NPP of three vegetation types using improved BEPS shows evident differences compared to that using original BEPS, with the highest difference ratio of 9.21 % in forest and the lowest value of 4.29 % in crop. The difference ratios between different vegetation types lie on the dependence on natural water sources. The modeled NPP in five geographic zones using improved BEPS is higher than those with original BEPS, with higher difference ratio in dry zones and lower value in wet zones. © 2013, Springer-Verlag Berlin Heidelberg.
Sanquist T.F.,Pacific Northwest National Laboratory |
Orr H.,Pacific Northwest National Laboratory |
Shui B.,5825 University Research Court |
Bittner A.C.,Bittner and Associates
Energy Policy | Year: 2012
A multivariate statistical approach to lifestyle analysis of residential electricity consumption is described and illustrated. Factor analysis of selected variables from the 2005 U.S. Residential Energy Consumption Survey (RECS) identified five lifestyle factors reflecting social and behavioral patterns associated with air conditioning, laundry usage, personal computer usage, climate zone of residence, and TV use. These factors were also estimated for 2001 RECS data. Multiple regression analysis using the lifestyle factors yields solutions accounting for approximately 40% of the variance in electricity consumption for both years. By adding the household and market characteristics of income, local electricity price and access to natural gas, variance accounted for is increased to approximately 54%. Income contributed ~1% unique variance to the models, indicating that lifestyle factors reflecting social and behavioral patterns better account for consumption differences than income. Geographic segmentation of factor scores shows distinct clusters of consumption and lifestyle factors, particularly in suburban locations. The implications for tailored policy and planning interventions are discussed in relation to lifestyle issues. © 2011 Elsevier Ltd.