Revisiting the weather effect on energy consumption: Implications for the impact of climate change

Kaufmann, Robert K.; Gopal, Sucharita; Tang, Xiaojing; Raciti, Steve M.; Lyons, Paul; Geron, Nick; Craig, Francis W.

doi:10.1016/j.enpol.2013.07.056

Cited by 37 publications

(24 citation statements)

References 19 publications

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“…Some results indicate that urbanization increases the number of energy utilization through changes in lifestyles, which means there is a positive relationship between urbanization and household energy consumption [42,51,53,55,57,65,71]. However, some studies provide the opposite result [34,42,67]. Other studies show some mixed results, indicating that the relationship between urbanization and household energy use is complex and remains inconclusive [38,40].…”

Section: Social Factorsmentioning

confidence: 80%

“…Thus, the temperature fluctuation in time series is used to examine its impact on HEC for the given region. The impact of temperature variations on energy consumption for heating and cooling is the most sensitive [10,11,32,34]. In this study, to reflect the regional differences, the temperature is regarded as the spatial function of energy consumption.…”

Section: Discussionmentioning

confidence: 99%

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Factors Influencing the Spatial Difference in Household Energy Consumption in China

Ding

Niu

et al. 2016

Sustainability

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Abstract:What factors determine the spatial heterogeneity of household energy consumption (HEC) in China? Can the impacts of these factors be quantified? What are the trends and characteristics of the spatial differences? To date, these issues are still unclear. Based on the STIRPAT model and panel dataset for 30 provinces in China over the period 1997-2013, this paper investigated influences of the income per capita, urbanization level and annual average temperature on HEC, and revealed the spatial effects of these influencing factors. The results show that the income level is the main influencing factor, followed by the annual average temperature. There exists a diminishing marginal contribution with increasing income. The influence of urbanization level varies according to income level. In addition, from the eastern region to western region of China, variances largely depend upon economic level at the provincial level. From the northern region to southern region, change is mainly caused by temperature. The urbanization level has more significant impact on the structure and efficiency of household energy consumption than on its quantity. These results could provide reference for policy making and energy planning.

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Section: Social Factorsmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Factors Influencing the Spatial Difference in Household Energy Consumption in China

Ding

Niu

et al. 2016

Sustainability

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“…Results indicate that the energy-temperature relationship is sensitive to the exact time of the day or night. Kaufman et al [26] argue that constructing degree days based on hourly values rather than daily values increases the accuracy of 'temporal downscaling of weather related energy use'.…”

Section: Literature Reviewmentioning

confidence: 99%

The impact of weather variation on energy consumption in residential houses

Fikru

Gautier

2015

Applied Energy

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“…Two exception areas where Stirling engines have been more successful are CHP and Solar power generation due to raised awareness in climate change by industry 14 , the deregulation of the power industry, and the aging of the centralized power infrastructure currently in use in many countries 15 . Technological issues hindering the development of Stirling engines include lubrication, weight and size, and control of the engine 12, 13 .…”

Section: Introductionmentioning

confidence: 99%

Stirling Engine Systems Tradespace Exploration Framework

Smirnov

Golkar

2015

Procedia Computer Science

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The purpose of this work is to propose a tradespace exploration framework for the design and system analysis of Stirling engines. The framework can be used to execute trade studies for different objectives. The framework obtains Pareto frontiers of optimal designs by evaluating performance (power, efficiency) and cost metrics for 750 Stirling engine design alternatives. The proposed system design framework integrates four domain models. An engine cycle thermodynamic model is to calculate engine cycle and performance parameters. A heat exchanger model is to estimate areas of heat exchangers. An economic model to predict the capital cost of the engine system. And a tradespace model based on five design parameters identified as drivers of the design: Stirling engine type (alpha, beta, or gamma), engine bore, stroke, hot space temperature, and cold space temperature. The results of the model shed insights on the design of Stirling engines and develop guidelines to identify the most suitable combinations of engine architectures and design parameters to address specific applications. The paper shows how the proposed framework can be applied to the design of Stirling engines for two different applications, namely waste heat recovery and combined heat and power generation for residential real estate units. Considering the identified customer needs for these two applications, the results of the study indicate that alpha-type Stirling engines are more appropriate for industrial waste heat recovery, e.g. heat treating furnaces and cement kilns, having a power output 50% higher for a given efficiency then other engine types. Beta-type engines result more beneficial for residential combined heat and power generation. Gamma-type engines result advantageous for low power applications such as toy models and tool kit engines.

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Revisiting the weather effect on energy consumption: Implications for the impact of climate change

Cited by 37 publications

References 19 publications

Factors Influencing the Spatial Difference in Household Energy Consumption in China

Factors Influencing the Spatial Difference in Household Energy Consumption in China

The impact of weather variation on energy consumption in residential houses

Stirling Engine Systems Tradespace Exploration Framework

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