Heat roadmap China: New heat strategy to reduce energy consumption towards 2030

Xiong, Weiming; Wang, Yu; Mathiesen, Brian Vad; Lund, Henrik; Zhang, Xiliang

doi:10.1016/j.energy.2014.12.039

Cited by 145 publications

(55 citation statements)

References 21 publications

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“…And to a certain extent, we can suppose that the unit energy consumption of the computing node is known, and cut down the execution time is helpful to reduce the energy consumption, so as to achieve the effect of energy-saving [6] . How to adjust the frequency of computing node in the resource layer to ensure that the tasks have been completed before deadlines will not be considered in this article.…”

Section: Correlation Analysismentioning

confidence: 99%

Research of Dependent Tasks Scheduling Algorithm in Cloud Computing Environments

et al. 2016

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With the dependent relationship of tasks submitted by the users in the model of Cloud computing resources scheduling become stronger and stronger, it is worthy of studying how to optimize the scheduling strategy and algorithm to meet the different demands of the users, and it is absolutely importance. In this article, the author analysed the factors that will affect the entire task-sets execution firstly. Then proposed a new tasks scheduling model based on the original priority calculation method and the idea of redundant duplication of tasks. In the phase of tasks scheduling in the model, the execution results of all parent tasks of the subtask that being executing are considered. The costs of communication between task-sets has reduced by the method of redundant duplication of tasks, so that the execution time of some subtasks share be advanced, and the entire execution efficiency of task-sets can be increased. At the end of this article, from the comparative results of the space-time complexity of contrast algorithms and the algorithm proposed by the author during the process of processing dependent tasks, we can find that subtasks execution time can be advanced and the complete time of the whole task-set can be cut down to a certain extent Dependent Tasks and the Resource Node ModellingThe dependent tasks are the tasks that in the task-sets submitted by users exist a certain dependent relationship between each other, and the relationship shows up in that before the subtask be executed, the execution results of its parent tasks must be known. And we can use a directed acyclic graph G to represent the dependent tasks [1]. As is shown in the following figure: The figure is represented as a four-tuples:, ,, , … , and | , ∈ , . The N represents the task node collection and the represents a single task node. The E represents the collection of "edge", and the edge determines the dependent relationship between tasks. If tow task nodes are connected by the edge, then there is a dependency relationship between them, and the sub task is the task node that the arrow points to, another side points to the parent task. The Q represents the average calculation cost collection for each task node, and the average calculation cost here refers to the average value of the cost of the task node in all resource nodes. The W represents the collection of communication cost between the tasks. And the special note here is that if tow dependencies are distributed to the same computing node, there is no communication cost between them [2].We can also make the following definition: Definition 1: pre v represents the predecessor task node set (or parent task node set) of the single task node v , and sub v represents the subsequent task node set (or sub task set) of the single task node v .Here we consider the computing node modelling next. Before that we need to do some declare first, the scheduling model of dependent tasks is consistent with the scheduling model of independent tasks, which has demonstrated in the

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Section: Correlation Analysismentioning

confidence: 99%

Research of Dependent Tasks Scheduling Algorithm in Cloud Computing Environments

et al. 2016

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“…This demarcation line roughly falls at 33.6°N, and along this line, the average January temperature is about 0°C (Almond et al 2009). North of this line, areas experience freezing cold winters, and therefore, the government provided free coal-powered heating for homes and offices, either directly or through state-owned enterprises; whereas due to budgetary constraints, southern China was denied (Almond et al 2009;Chen et al 2013a;Xiong et al 2015). The rudiments of this policy still remain as some homes and offices continue to receive free heating (Almond et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…Since the commencement of the Huai River Policy, the government has established a district heating system (DHS) in the north, whereas in southern China, individual heating units like air conditioners and electric ventilation fans were used in summer and during cold winters (Xiong et al 2015). Due to increasing energy demand and massive urbanisation, the building area served by DHS has increased dramatically in recent years, from 277 M m 2 in 1991 to 4357 M m 2 in 2010.…”

Section: Introductionmentioning

confidence: 99%

“…During that time, the Chinese government divided the country into northern and southern China, separated by Huai River and Qinling Mountains (Almond et al 2009;Chen et al 2013a;Xiong et al 2015). This demarcation line roughly falls at 33.6°N, and along this line, the average January temperature is about 0°C (Almond et al 2009).…”

Section: Introductionmentioning

confidence: 99%

“…This policy, commonly known as the Huai River Policy (Chen et al 2013a;Xiong et al 2015), is deemed unfair by many southern residents, especially those in Nanjing, Shanghai and Chengdu just south of the Huai River, where winters can be severe (Almond et al 2009). Unlike northern China, heating systems did not exist in southern China, as there was no infrastructure support from government or the private sector (Chen et al 2013a).…”

Section: Introductionmentioning

confidence: 99%

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Dynamism of household carbon emissions (HCEs) from rural and urban regions of northern and southern China

Maraseni

Bian

et al. 2016

Environ Sci Pollut Res

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China contributes 23 % of global carbon emissions, of which 26 % originate from the household sector. Due to vast variations in both climatic conditions and the affordability and accessibility of fuels, household carbon emissions (HCEs) differ significantly across China. This study compares HCEs (per person) from urban and rural regions in northern China with their counterparts in southern China. Annual macroeconomic data for the study period 2005 to 2012 were obtained from Chinese government sources, whereas the direct HCEs for different types of fossil fuels were obtained using the IPCC reference approach, and indirect HCEs were calculated by input-output analysis. Results suggest that HCEs from urban areas are higher than those from rural areas. Regardless of the regions, there is a similarity in per person HCEs in urban areas, but the rural areas of northern China had significantly higher HCEs than those from southern China. The reasons for the similarity between urban areas and differences between rural areas and the percentage share of direct and indirect HCEs from different sources are discussed. Similarly, the reasons and solutions to why decarbonising policies are working in urban areas but not in rural areas are discussed.

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